Journal articles on the topic 'Independent Component Analysis (ICA)'
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1
Unnisa, Yaseen, Danh Tran, and Fu Chun Huang. "Statistical Independence and Independent Component Analysis." Applied Mechanics and Materials 553 (May 2014): 564–69. http://dx.doi.org/10.4028/www.scientific.net/amm.553.564.
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Independent Component Analysis (ICA) is a recent method of blind source separation, it has been employed in medical image processing and structural damge detection. It can extract source signals and the unmixing matrix of the system using mixture signals only. This novel method relies on the assumption that source signals are statistically independent. This paper looks at various measures of statistical independence (SI) employed in ICA, the measures proposed by Bakirov and his associates, and the effects of levels of SI of source signals on the output of ICA. Firstly, two statistical independent signals in the form of uniform random signals and a mixing matrix were used to simulate mixture signals to be anlysed byfastICApackage, secondly noise was added onto the signals to investigate effects of levels of SI on the output of ICA in the form of soure signals, the mixing and unmixing matrix. It was found that for p-value given by Bakirov’s SI statistical testing of the null hypothesis H0is a good indication of the SI between two variables and that for p-value larger than 0.05, fastICA performs satisfactorily.
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Mahmoudishadi, S., A. Malian, and F. Hosseinali. "COMPARING INDEPENDENT COMPONENT ANALYSIS WITH PRINCIPLE COMPONENT ANALYSIS IN DETECTING ALTERATIONS OF PORPHYRY COPPER DEPOSIT (CASE STUDY: ARDESTAN AREA, CENTRAL IRAN)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 161–66. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-161-2017.
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The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) has been evaluated for the visible and near-infrared (VNIR) and Shortwave infrared (SWIR) subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6) were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.
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Suzuki, Taiji, and Masashi Sugiyama. "Least-Squares Independent Component Analysis." Neural Computation 23, no. 1 (January 2011): 284–301. http://dx.doi.org/10.1162/neco_a_00062.
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Accurately evaluating statistical independence among random variables is a key element of independent component analysis (ICA). In this letter, we employ a squared-loss variant of mutual information as an independence measure and give its estimation method. Our basic idea is to estimate the ratio of probability densities directly without going through density estimation, thereby avoiding the difficult task of density estimation. In this density ratio approach, a natural cross-validation procedure is available for hyperparameter selection. Thus, all tuning parameters such as the kernel width or the regularization parameter can be objectively optimized. This is an advantage over recently developed kernel-based independence measures and is a highly useful property in unsupervised learning problems such as ICA. Based on this novel independence measure, we develop an ICA algorithm, named least-squares independent component analysis.
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Zhan, Xin Wu, and Wu Jiao Dai. "Dam Deformation Analysis Based on Independent Component Analysis." Applied Mechanics and Materials 212-213 (October 2012): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.212-213.859.
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Independent component analysis (ICA) is a recent and well-known technique used to separate mixtures of signals. It can separate independent components from mixed signals and has many advantages in blind signal separation, redundancy removal and processing of frequency aliasing problems. Deformation monitoring data can be regarded as the digital signals series which is composed of different frequency. After making test on ICA in processing dam observation data we can draw a conclusion that it is practical and applicative for ICA to evaluate the stability of the dam and reflect the working condition of dam.
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Bellini, Fabio, and Ernesto Salinelli. "Independent Component Analysis and Immunization: An Exploratory Study." International Journal of Theoretical and Applied Finance 06, no. 07 (November 2003): 721–38. http://dx.doi.org/10.1142/s0219024903002201.
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In this work we apply Independent Component Analysis (ICA) to the identification of the factors driving spot rates curve movements. A comparison between the standard Principal Components Analysis (PCA) approach and ICA is carried out both from a theoretical point of view, critically analyzing the negentropy based approach to ICA, and from an empirical point of view, where the performance of immunization strategies based on PCA and ICA are tested.
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Honório, Bruno César Zanardo, Alexandre Cruz Sanchetta, Emilson Pereira Leite, and Alexandre Campane Vidal. "Independent component spectral analysis." Interpretation 2, no. 1 (February 1, 2014): SA21—SA29. http://dx.doi.org/10.1190/int-2013-0074.1.
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Spectral decomposition techniques can break down the broadband seismic records into a series of frequency components that are useful for seismic interpretation and reservoir characterization. However, it is laborious and time-consuming to analyze and to interpret each seismic frequency volume taking all the usable seismic bandwidth. In this context, we propose a multivariate technique based on independent component analysis (ICA) with the goal of choosing the spectral components that best represent the whole seismic spectrum while keeping the main geological information. The ICA-based method goes beyond the Gaussian assumption and takes advantage of higher order statistics to find a new set of variables that are independent of each other. The independence between two components is a more general statistical concept than the noncorrelation and, in principle, allows the extraction of more significant information from the data. We have tested four different contrast functions to estimate the independent components (ICs), which we could verify a better channel system identification depending on the contrast function used. By stacking the ICs in the red-green-blue color space, we could represent the main information in a single, good quality image. To illustrate the proposed method, we have applied it to a seismic volume which was acquired over the F3 block in the Dutch sector of the North Sea. We also compared the results with those obtained by principal component analysis. In this case, the ICA-based method could generate a better image and faithfully delineate a channel system presented in the studied seismic volume.
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Ke, Qiao, Jiangshe Zhang, H. M. Srivastava, Wei Wei, and Guang-Sheng Chen. "Independent Component Analysis Based on Information Bottleneck." Abstract and Applied Analysis 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/386201.
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The paper is mainly used to provide the equivalence of two algorithms of independent component analysis (ICA) based on the information bottleneck (IB). In the viewpoint of information theory, we attempt to explain the two classical algorithms of ICA by information bottleneck. Furthermore, via the numerical experiments with the synthetic data, sonic data, and image, ICA is proved to be an edificatory way to solve BSS successfully relying on the information theory. Finally, two realistic numerical experiments are conducted via FastICA in order to illustrate the efficiency and practicality of the algorithm as well as the drawbacks in the process of the recovery images the mixing images.
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KARHUNEN, JUHA, SIMONA MĂlĂROIU, and MIKA ILMONIEMI. "LOCAL LINEAR INDEPENDENT COMPONENT ANALYSIS BASED ON CLUSTERING." International Journal of Neural Systems 10, no. 06 (December 2000): 439–51. http://dx.doi.org/10.1142/s0129065700000429.
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In standard Independent Component Analysis (ICA), a linear data model is used for a global description of the data. Even though linear ICA yields meaningful results in many cases, it can provide a crude approximation only for general nonlinear data distributions. In this paper a new structure is proposed, where local ICA models are used in connection with a suitable grouping algorithm clustering the data. The clustering part is responsible for an overall coarse nonlinear representation of the data, while linear ICA models of each cluster are used for describing local features of the data. The goal is to represent the data better than in linear ICA while avoiding computational difficulties related with nonlinear ICA. Several data grouping methods are considered, including standard K-means clustering, self-organizing maps, and neural gas. Connections to existing methods are discussed, and experimental results are given for artificial data and natural images. Furthermore, a general theoretical framework encompassing a large number of methods for representing data is introduced. These range from global, dense representation methods to local, very sparse coding methods. The proposed local ICA methods lie between these two extremes.
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MEYER-BÄSE, ANKE, OLIVER LANGE, AXEL WISMÜLLER, and HELGE RITTER. "MODEL-FREE FUNCTIONAL MRI ANALYSIS USING TOPOGRAPHIC INDEPENDENT COMPONENT ANALYSIS." International Journal of Neural Systems 14, no. 04 (August 2004): 217–28. http://dx.doi.org/10.1142/s0129065704002017.
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Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.
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Erdogmus, Deniz, Kenneth E. Hild, Yadunandana N. Rao, and José C. Príncipe. "Minimax Mutual Information Approach for Independent Component Analysis." Neural Computation 16, no. 6 (June 1, 2004): 1235–52. http://dx.doi.org/10.1162/089976604773717595.
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Minimum output mutual information is regarded as a natural criterion for independent component analysis (ICA) and is used as the performance measure in many ICA algorithms. Two common approaches in information-theoretic ICA algorithms are minimum mutual information and maximum output entropy approaches. In the former approach, we substitute some form of probability density function (pdf) estimate into the mutual information expression, and in the latter we incorporate the source pdf assumption in the algorithm through the use of nonlinearities matched to the corresponding cumulative density functions (cdf). Alternative solutions to ICA use higher-order cumulant-based optimization criteria, which are related to either one of these approaches through truncated series approximations for densities. In this article, we propose a new ICA algorithm motivated by the maximum entropy principle (for estimating signal distributions). The optimality criterion is the minimum output mutual information, where the estimated pdfs are from the exponential family and are approximate solutions to a constrained entropy maximization problem. This approach yields an upper bound for the actual mutual information of the output signals—hence, the name minimax mutual information ICA algorithm. In addition, we demonstrate that for a specific selection of the constraint functions in the maximum entropy density estimation procedure, the algorithm relates strongly to ICA methods using higher-order cumulants.
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Cui, Jin Ling, Ming Deng, Jian En Jing, and En Ci Wang. "Using Independent Component Analysis to Process Magnetotelluric Data." Applied Mechanics and Materials 295-298 (February 2013): 2795–98. http://dx.doi.org/10.4028/www.scientific.net/amm.295-298.2795.
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It is much more difficult to estimate magnetotelluric(MT) impedance tensor in the sites which are contaminated by high noise. In order to estimate a precise impedance tensor, we examine a new method called independent component analysis (ICA) that is developed to remove the noise in the recorded data. ICA is a time series analysis method, in which complicated data sets can be separated into all underlying sources without knowing these sources or the way that they are mixed. In this paper, we use the ICA method to process real MT data. All results show that apparent resistivity and phases which are preprocessed by ICA and derived from impedance tensors are generally more stable than only robust processing. These results reveal that ICA has the potential to handle noisy data.
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Xiao, Ying Wang, and Ying Du. "Combination Method of Kernel Principal Component Analysis and Independent Component Analysis for Process Monitoring." Applied Mechanics and Materials 249-250 (December 2012): 153–58. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.153.
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A combination method of kernel principal component analysis (KPCA) and independent component analysis (ICA) for process monitoring is proposed. The new method is a two-phase algorithm: whitened KPCA plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the Tennessee Eastman (TE) simulated process indicates that the proposed process monitoring method can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.
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Borek, Dominika, Raquel Bromberg, Johan Hattne, and Zbyszek Otwinowski. "Real-space analysis of radiation-induced specific changes with independent component analysis." Journal of Synchrotron Radiation 25, no. 2 (February 22, 2018): 451–67. http://dx.doi.org/10.1107/s1600577517018148.
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A method of analysis is presented that allows for the separation of specific radiation-induced changes into distinct components in real space. The method relies on independent component analysis (ICA) and can be effectively applied to electron density maps and other types of maps, provided that they can be represented as sets of numbers on a grid. Here, for glucose isomerase crystals, ICA was used in a proof-of-concept analysis to separate temperature-dependent and temperature-independent components of specific radiation-induced changes for data sets acquired from multiple crystals across multiple temperatures. ICA identified two components, with the temperature-independent component being responsible for the majority of specific radiation-induced changes at temperatures below 130 K. The patterns of specific temperature-independent radiation-induced changes suggest a contribution from the tunnelling of electron holes as a possible explanation. In the second case, where a group of 22 data sets was collected on a single thaumatin crystal, ICA was used in another type of analysis to separate specific radiation-induced effects happening on different exposure-level scales. Here, ICA identified two components of specific radiation-induced changes that likely result from radiation-induced chemical reactions progressing with different rates at different locations in the structure. In addition, ICA unexpectedly identified the radiation-damage state corresponding to reduced disulfide bridges rather than the zero-dose extrapolated state as the highest contrast structure. The application of ICA to the analysis of specific radiation-induced changes in real space and the data pre-processing for ICA that relies on singular value decomposition, which was used previously in data space to validate a two-component physical model of X-ray radiation-induced changes, are discussed in detail. This work lays a foundation for a better understanding of protein-specific radiation chemistries and provides a framework for analysing effects of specific radiation damage in crystallographic and cryo-EM experiments.
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Li, Yi-Ou, Tülay Adali, and Vince D. Calhoun. "A Feature-Selective Independent Component Analysis Method for Functional MRI." International Journal of Biomedical Imaging 2007 (2007): 1–12. http://dx.doi.org/10.1155/2007/15635.
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In this work, we propose a simple and effective scheme to incorporate prior knowledge about the sources of interest (SOIs) in independent component analysis (ICA) and apply the method to estimate brain activations from functional magnetic resonance imaging (fMRI) data. We name the proposed method as feature-selective ICA since it incorporates the features in the sample space of the independent components during ICA estimation. The feature-selective scheme is achieved through a filtering operation in the source sample space followed by a projection onto the demixing vector space by a least squares projection in an iterative ICA process. We perform ICA estimation of artificial activations superimposed into a resting state fMRI dataset to show that the feature-selective scheme improves the detection of injected activation from the independent component estimated by ICA. We also compare the task-related sources estimated from true fMRI data by a feature-selective ICA algorithm versus an ICA algorithm and show evidence that the feature-selective scheme helps improve the estimation of the sources in both spatial activation patterns and the time courses.
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Ibrahim, Wan Nurhidayah, Mohd Syahid Anuar, Ali Selamat, and Ondrej Krejcar. "BOTNET DETECTION USING INDEPENDENT COMPONENT ANALYSIS." IIUM Engineering Journal 23, no. 1 (January 4, 2022): 95–115. http://dx.doi.org/10.31436/iiumej.v23i1.1789.
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Botnet is a significant cyber threat that continues to evolve. Botmasters continue to improve the security framework strategy for botnets to go undetected. Newer botnet source code runs attack detection every second, and each attack demonstrates the difficulty and robustness of monitoring the botnet. In the conventional network botnet detection model that uses signature-analysis, the patterns of a botnet concealment strategy such as encryption & polymorphic and the shift in structure from centralized to decentralized peer-to-peer structure, generate challenges. Behavior analysis seems to be a promising approach for solving these problems because it does not rely on analyzing the network traffic payload. Other than that, to predict novel types of botnet, a detection model should be developed. This study focuses on using flow-based behavior analysis to detect novel botnets, necessary due to the difficulties of detecting existing patterns in a botnet that continues to modify the signature in concealment strategy. This study also recommends introducing Independent Component Analysis (ICA) and data pre-processing standardization to increase data quality before classification. With and without ICA implementation, we compared the percentage of significant features. Through the experiment, we found that the results produced from ICA show significant improvements. The highest F-score was 83% for Neris bot. The average F-score for a novel botnet sample was 74%. Through the feature importance test, the feature importance increased from 22% to 27%, and the training model false positive rate also decreased from 1.8% to 1.7%. ABSTRAK: Botnet merupakan ancaman siber yang sentiasa berevolusi. Pemilik bot sentiasa memperbaharui strategi keselamatan bagi botnet agar tidak dapat dikesan. Setiap saat, kod-kod sumber baru botnet telah dikesan dan setiap serangan dilihat menunjukkan tahap kesukaran dan ketahanan dalam mengesan bot. Model pengesanan rangkaian botnet konvensional telah menggunakan analisis berdasarkan tanda pengenalan bagi mengatasi halangan besar dalam mengesan corak botnet tersembunyi seperti teknik penyulitan dan teknik polimorfik. Masalah ini lebih bertumpu pada perubahan struktur berpusat kepada struktur bukan berpusat seperti rangkaian rakan ke rakan (P2P). Analisis tingkah laku ini seperti sesuai bagi menyelesaikan masalah-masalah tersebut kerana ianya tidak bergantung kepada analisis rangkaian beban muatan trafik. Selain itu, bagi menjangka botnet baru, model pengesanan harus dibangunkan. Kajian ini bertumpu kepada penggunaan analisa tingkah-laku berdasarkan aliran bagi mengesan botnet baru yang sukar dikesan pada corak pengenalan botnet sedia-ada yang sentiasa berubah dan menggunakan strategi tersembunyi. Kajian ini juga mencadangkan penggunakan Analisis Komponen Bebas (ICA) dan pra-pemprosesan data yang standard bagi meningkatkan kualiti data sebelum pengelasan. Peratusan ciri-ciri penting telah dibandingkan dengan dan tanpa menggunakan ICA. Dapatan kajian melalui eksperimen menunjukkan dengan penggunaan ICA, keputusan adalah jauh lebih baik. Skor F tertinggi ialah 83% bagi bot Neris. Purata skor F bagi sampel botnet baru adalah 74%. Melalui ujian kepentingan ciri, kepentingan ciri meningkat dari 22% kepada 27%, dan kadar positif model latihan palsu juga berkurangan dari 1.8% kepada 1.7%.
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Wang, Jianying, Cheng Wang, Tianshu Zhang, and Bineng Zhong. "Comparison of Different Independent Component Analysis Algorithms for Output-Only Modal Analysis." Shock and Vibration 2016 (2016): 1–25. http://dx.doi.org/10.1155/2016/6309084.
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From the principle of independent component analysis (ICA) and the uncertainty of amplitude, order, and number of source signals, this paper expounds the root reasons for modal energy uncertainty, identified order uncertainty, and modal missing in output-only modal analysis based on ICA methods. Aiming at the problem of lack of comparison and evaluation of different ICA algorithms for output-only modal analysis, this paper studies the different objective functions and optimization methods of ICA for output-only modal parameter identification. Simulation results on simply supported beam verify the effectiveness, robustness, and convergence rate of five different ICA algorithms for output-only modal parameters identification and show that maximization negentropy with quasi-Newton iterative of ICA method is more suitable for modal parameter identification.
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Zhao, Yong Jian, Bo Qiang Liu, and Hong Run Wang. "Robust Method via Independent Component Analysis with Additive Noise." Advanced Materials Research 113-116 (June 2010): 272–75. http://dx.doi.org/10.4028/www.scientific.net/amr.113-116.272.
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Blind source separation via independent component analysis (ICA) has received increasing attention because of its potential application in signal processing system. The existing ICA methods can not give a consistent estimator of the mixing matrix because of additive noise. Based on interpretation and properties of the vectorial spaces of sources and mixtures, a new ICA method is presented in this paper that may constructively reject noise so as to estimate the mixing matrix consistently. This procedure may capture the underlying source dynamics effectively even if additive noise exists. The simulation results show that this method has high stability and reliability in the process of revealing the undering group structure of extracted ICA components.
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Li, Xiangshun, Di Wei, Cheng Lei, Zhiang Li, and Wenlin Wang. "Statistical Process Monitoring with Biogeography-Based Optimization Independent Component Analysis." Mathematical Problems in Engineering 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/1729612.
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Independent Component Analysis (ICA), a type of typical data-driven fault detection techniques, has been widely applied for monitoring industrial processes. FastICA is a classical algorithm of ICA, which extracts independent components by using the Newton iteration method. However, the choice of the initial iterative point of Newton iteration method is difficult; sometimes, selection of different initial iterative points tends to show completely different effects for fault detection. So far, there is still no good strategy to get an ideal initial iterative point for ICA. To solve this problem, a modified ICA algorithm based on biogeography-based optimization (BBO) called BBO-ICA is proposed for the purpose of multivariate statistical process monitoring. The Newton iteration method is replaced with BBO here for extracting independent components. BBO is a novel and effective optimization method to search extremes or maximums. Comparing with the traditional intelligent optimization algorithm of particle swarm optimization (PSO) and so on, BBO behaves with stronger capability and accuracy of searching for solution space. Moreover, numerical simulations are finished with the platform of DAMADICS. Results demonstrate the practicability and effectiveness of BBO-ICA. The proposed BBO-ICA shows better performance of process monitoring than FastICA and PSO-ICA for DAMADICS.
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Liu, Zhi-Yong, Kai-Chun Chiu, and Lei Xu. "One-Bit-Matching Conjecture for Independent Component Analysis." Neural Computation 16, no. 2 (February 1, 2004): 383–99. http://dx.doi.org/10.1162/089976604322742074.
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The one-bit-matching conjecture for independent component analysis (ICA) could be understood from different perspectives but is basically stated as “all the sources can be separated as long as there is a one-toone same-sign-correspondence between the kurtosis signs of all source probability density functions (pdf's) and the kurtosis signs of all model pdf's” (Xu, Cheung, & Amari, 1998a). This conjecture has been widely believed in the ICA community and implicitly supported by many ICA studies, such as the Extended Infomax (Lee, Girolami, & Sejnowski, 1999) and the soft switching algorithm (Welling & Weber, 2001). However, there is no mathematical proof to confirm the conjecture theoretically. In this article, only skewness and kurtosis are considered, and such a mathematical proof is given under the assumption that the skewness of the model densities vanishes. Moreover, empirical experiments are demonstrated on the robustness of the conjecture as the vanishing skewness assumption breaks. As a by-product, we also show that the kurtosis maximization criterion (Moreau & Macchi, 1996) is actually a special case of the minimum mutual information criterion for ICA.
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Yan, Yang, Sheng Li, and Xiao Long Jin. "Application of Independent Component Analysis in Structural Condition Identification." Applied Mechanics and Materials 644-650 (September 2014): 4061–65. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4061.
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One kind of structure condition identification method based on Independent Component Analysis (ICA) is proposed in this paper. The change of structure condition is identified by the feature index extracted from structure vibration signals. It is effectual to identify the structure condition by transforming the test signals into the statistic characteristic space. The correlation coefficient is introduced to measure the correlative degree between the statistical characteristic of two structure conditions and the damage characteristic extraction index based on ICA is constructed. Considering the structure actual situation, assume the undamaged condition or small damaged condition as the benchmark condition. The analysis of structure vibration experiment indicate that the ICA characteristic index can identify the difference between damaged and benchmark condition and this method of ICA is practicable.
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Dong, Guo Hua, Yan Li, and De Wen Hu. "Bridging a Gap in Independent Component Analysis." Applied Mechanics and Materials 239-240 (December 2012): 1279–83. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1279.
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Independent Component Analysis (ICA) is a powerful method which aims at representing a given random signal as a sum of independent sources. The engineering community, however, works at the distribution function or characteristic function level while makes assertions at the random variable level. This legitimacy of this jump has never been established and consists of a longstanding gap in the ICA literature. In this paper, it is proved that existence of a factorization of characteristic function does imply existence of a corresponding decomposition of random variable into independent sum and thus the gap is bridged. The proof relies on two nontrivial results from probability theory.
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JENTZSCH, INES. "INDEPENDENT COMPONENT ANALYSIS SEPARATES SEQUENCE-SENSITIVE ERP COMPONENTS." International Journal of Bifurcation and Chaos 14, no. 02 (February 2004): 667–78. http://dx.doi.org/10.1142/s0218127404009363.
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Human performance is strongly influenced by the sequence of events. Decreasing the response-stimulus interval (RSI) between events qualitatively changes these so-called sequential effects. Using event-related brain potentials (ERPs) to detect electrical brain activity related to sequential patterns helps to uncover mechanisms underlying the observed performance data. Using a spatial compatible two-choice task ERPs were recorded from 32 electrode sites and Independent Component Analysis (ICA) applied to separate sequence-sensitive ERP components from two experiments, involving different RSIs. Independent Component Analysis was able to separate temporally and spatially overlapping ERP components. Sensitivity to the sequence of preceding events could be revealed in an early subcomponent of the N100 complex. Moreover, and in line with earlier reports sequential effects were also observed in P300 subcomponents.
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Mendhurwar, Kaustubha, Shivaji Patil, Harsh Sundani, Priyanka Aggarwal, and Vijay Devabhaktuni. "Edge-Detection in Noisy Images Using Independent Component Analysis." ISRN Signal Processing 2011 (April 10, 2011): 1–9. http://dx.doi.org/10.5402/2011/672353.
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Edges in a digital image provide important information about the objects contained within the image since they constitute boundaries between objects in the image. This paper proposes a new approach based on independent component analysis (ICA) for edge-detection in noisy images. The proposed approach works in two phases—the training phase and the edge-detection phase. The training phase is carried out only once to determine parameters for the ICA. Once calculated, these ICA parameters can be employed for edge-detection in any number of noisy images. The edge-detection phase deals with transitioning in and out of ICA domain and recovering the original image from a noisy image. Both gray scale as well as colored images corrupted with Gaussian noise are studied using the proposed approach, and remarkably improved results, compared to the existing edge-detection techniques, are achieved. Performance evaluation of the proposed approach using both subjective as well as objective methods is presented.
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Cheung, Yiu-ming, and Lei Xu. "Independent component ordering in ICA time series analysis." Neurocomputing 41, no. 1-4 (October 2001): 145–52. http://dx.doi.org/10.1016/s0925-2312(00)00358-1.
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Rahmatiara Putri, Silvya, and Sutawanir Darwis. "Visualisasi Kerusakan Bearing Menggunakan Metode Independent Component Analysis (ICA)." Bandung Conference Series: Statistics 2, no. 2 (July 29, 2022): 299–307. http://dx.doi.org/10.29313/bcss.v2i2.4236.
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Abstract. Vibration is a response of a mechanical system either caused by a given excitation force or changes in operating conditions as a function of time. The force that causes this vibration can be caused by several sources such as contact/impact between moving/rotating components, rotation of an unbalanced mass, misalignment and also Bearing faults which will be the topic of this research. The data used is Bearing vibration data obtained from the Prognostics Center of Excellence (PcoE) through prognostic data storage donated by the Intelligent Maintenance System (IMS), University of Cincinnati in 2003. Principal Component Analysis (PCA) method is used to see how many components resulting from. Furthermore, the selected component from the Principal Component (PC) becomes the basis for the component results from the Independent Component Analysis (ICA) which is used to visually see the distribution of data. In this thesis presents ICA and compare with Principal Component Analysis (PCA). In the visual results of the plot of the Principal Component and Independent Component Bearing damage, it can be identified that each damage produces a different form of vibration after being reduced.
Abstrak. Getaran merupakan respon dari sebuah sistem mekanik baik yang diakibatkan oleh gaya eksitasi yang diberikan maupun perubahan kondisi operasi sebagai fungsi waktu. Gaya yang menyebabkan getaran ini dapat ditimbulkan oleh beberapa sumber misalnya kontak/benturan antar komponen yang bergerak/berputar, putaran dari massa yang tidak seimbang (unbalance mass), misalignment dan juga karena kerusakan bantalan (Bearing fault) yang akan menjadi topik penelitian ini. Data yang digunakan yaitu data vibrasi Bearing yang diperoleh dari Prognostics Center of Excellence (PcoE) melalui penyimpanan data prognostik yang disumbangkan oleh Intelligent Maintenance System (IMS), University of Cincinnati pada tahun 2003. Metode Analisis Komponen Utama (AKU) digunakan untuk melihat berapa komponen yang dihasilkan. Selanjutnya komponen terpilih dari Komponen Utama (KU) menjadi dasar untuk hasil komponen dari Independent Component Analysis (ICA) yang digunakan untuk melihat sebaran data dengan visual oleh plot, sehingga menghasilkan beberapa komponen. Dalam skripsi ini akan disajikan ICA dalam statistik dan bandingkan metode ini dengan Analisis Komponen Utama (AKU). Pada hasil visual plot Komponen Utama dan Independent Component kerusakan Bearing dapat diidentifikasi bahwa pada setiap kerusakan menghasilkan bentuk getaran yang berbeda-beda setelah direduksi.
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WU, EDMOND HAOCUN, and PHILIP L. H. YU. "PATTERN RECOGNITION OF THE TERM STRUCTURE USING INDEPENDENT COMPONENT ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 02 (March 2006): 173–88. http://dx.doi.org/10.1142/s0218001406004594.
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Term structure is a useful curve describing some financial asset as a function of time to maturity or expiration. In this paper, we propose to use Independent Component Analysis (ICA) to model the term structure of multiple yield curves. The idea is that we first employ ICA to decompose the multivariate time series, then we suggest two ICA methods for dimension reduction and pattern recognition of the term structure. We also compare the results by using an alternative method, Principal Component Analysis (PCA). The empirical studies suggest that the proposed ICA approaches outperform PCA methods in modeling the term structure. This model can be used in financial time series analysis as well as related financial applications.
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Do, Cuong Manh, and Bahram Javidi. "Three-dimensional computational holographic imaging and recognition using independent component analysis." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2090 (November 27, 2007): 409–22. http://dx.doi.org/10.1098/rspa.2007.0167.
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We present computational holographic three-dimensional imaging and automated object recognition based on independent component analysis (ICA). Three-dimensional sensing of the scene is performed by computational holographic imaging of the objects using phase-shifting digital holography. We used principal components analysis to reduce data dimension and ICA to recognize the three-dimensional objects. In this paper, kurtosis maximization-based algorithm is used. To the best of our knowledge, this paper is the first to report using ICA in three-dimensional imaging technology.
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Sompairac, Nicolas, Petr V. Nazarov, Urszula Czerwinska, Laura Cantini, Anne Biton, Askhat Molkenov, Zhaxybay Zhumadilov, et al. "Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets." International Journal of Molecular Sciences 20, no. 18 (September 7, 2019): 4414. http://dx.doi.org/10.3390/ijms20184414.
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Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.
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Abdel-Hamid, Gamal Mabrouk, and Reham S. Saad. "Blind Channel Estimation Using Wavelet Denoising of Independent Component Analysis for LTE." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (January 1, 2016): 126. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp126-137.
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<p>A new proposal of blind channel estimation method for long term evoluation (LTE) based on combining advantages of denoising property of wavelet transform (WT) with blind estimation capability of independent component analysis (ICA) called wavelet denoising of ICA (WD-ICA) was presented. This new method increased the spectral efficiency compared to training based methods, and provided considerable performance enhancement over conventional ICA methods. The conventional blind channel estimation methods based on ICA were performed individually for each orthogonal frequency division multiplexing (OFDM) subcarrier. To reduce complexity of implementation of WD-ICA method, channel interpolation was used. This method was presented for multiple-input-multiple-output (MIMO) downlink LTE system. WD-ICA method was compared to conventional ICA methods and the Performance was evaluated by calculating normalized mean square error (NMSE) and bit error rate (BER). WD-ICA method as compared to the other known ICA channel estimation methods has lower complexity, lower value of NMSE, and lower value of BER, which indicates the superiority of the proposed method.</p>
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Lörincz, András, and Barnabás Póczos. "Cost Component Analysis." International Journal of Neural Systems 13, no. 03 (June 2003): 183–92. http://dx.doi.org/10.1142/s0129065703001558.
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In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors (FAPPs) have been suggested to overcome this problem. Here, a novel FAPP, cost component analysis (CCA) is described. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape. That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis (ICA). The advantage of CCA is that each independent ICA component can be optimized separately. In turn, (i) CCA intends to partition the original problem into subproblems and (ii) separating (partitioning) the original optimization problem into subproblems may serve interpretation. Most importantly, (iii) CCA may give rise to high gains in optimization time. Numerical simulations illustrate the working of the algorithm.
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Al-Saegh, Ali. "Comparison of Complex-Valued Independent Component Analysis Algorithms for EEG Data." Iraqi Journal for Electrical and Electronic Engineering 15, no. 1 (June 1, 2019): 1–12. http://dx.doi.org/10.37917/ijeee.15.1.1.
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Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complexvalued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.
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Wei, Pengxu, Ruixue Bao, and Yubo Fan. "Comparing the reliability of different ICA algorithms for fMRI analysis." PLOS ONE 17, no. 6 (June 27, 2022): e0270556. http://dx.doi.org/10.1371/journal.pone.0270556.
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Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.
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Ventouras, Erricos M., Periklis Y. Ktonas, Hara Tsekou, Thomas Paparrigopoulos, Ioannis Kalatzis, and Constantin R. Soldatos. "Independent Component Analysis for Source Localization of EEG Sleep Spindle Components." Computational Intelligence and Neuroscience 2010 (2010): 1–12. http://dx.doi.org/10.1155/2010/329436.
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Sleep spindles are bursts of sleep electroencephalogram (EEG) quasirhythmic activity within the frequency band of 11–16 Hz, characterized by progressively increasing, then gradually decreasing amplitude. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG and visual selection of Independent Components (ICs), spindle “components” (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying these SCs. Current source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied to the original and the ICA-reconstructed EEGs. Results indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis of ICs. The intracranial current sources related to the SCs were found to be spatially stable during the time evolution of the sleep spindles.
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Hien, Thai Duy, Zensho Nakao, and Yen-Wei Chen. "Intelligent Logo Watermarking Based on Independent Component Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 390–96. http://dx.doi.org/10.20965/jaciii.2004.p0390.
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We present new intelligent logo watermarking based on independent component analysis (ICA) in which a binary logo watermark is embedded in a host image in a wavelet domain. To improve robustness, an image adaptive watermarking algorithm is applied by a stochastic approach based on a noise visibility function (NVF). The algorithm design, evaluation, and experimentation are described. Experimental results show that the logo watermark is perfectly extracted by ICA with excellent invisibility and with robustness against various image and digital processing operators and almost all compression algorithms such as Jpeg, jpeg 2000, SPIHT, EZW, and principal component analysis (PCA) based compression.
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de Borman, Aurélie, Simone Vespa, Riëm El Tahry, and P. A. Absil. "Estimation of seizure onset zone from ictal scalp EEG using independent component analysis in extratemporal lobe epilepsy." Journal of Neural Engineering 19, no. 2 (March 10, 2022): 026005. http://dx.doi.org/10.1088/1741-2552/ac55ad.
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Abstract Objective. The purpose of this study is to localize the seizure onset zone of patients suffering from drug-resistant epilepsy. During the last two decades, multiple studies proposed the use of independent component analysis (ICA) to analyze ictal electroencephalogram (EEG) recordings. This study aims at evaluating ICA potential with quantitative measurements. In particular, we address the challenging step where the components extracted by ICA of an ictal nature must be selected. Approach. We considered a cohort of 10 patients suffering from extratemporal lobe epilepsy who were rendered seizure-free after surgery. Different sets of pre-processing parameters were compared and component features were explored to help distinguish ictal components from others. Quantitative measurements were implemented to determine whether some of the components returned by ICA were located within the resection zone and thus likely to be ictal. Finally, an assistance to the component selection was proposed based on the implemented features. Main results. For every seizure, at least one component returned by ICA was localized within the resection zone, with the optimal pre-processing parameters. Three features were found to distinguish components localized within the resection zone: the dispersion of their active brain sources, the ictal rhythm power and the contribution to the EEG variance. Using the implemented component selection assistance based on the features, the probability that the first proposed component yields an accurate estimation reaches 51.43% (without assistance: 24.74%). The accuracy reaches 80% when considering the best result within the first five components. Significance. This study confirms the utility of ICA for ictal EEG analysis in extratemporal lobe epilepsy, and suggests relevant features to analyze the components returned by ICA. A component selection assistance is proposed to guide clinicians in their choice for ictal components.
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Eichele, Tom, Srinivas Rachakonda, Brage Brakedal, Rune Eikeland, and Vince D. Calhoun. "EEGIFT: Group Independent Component Analysis for Event-Related EEG Data." Computational Intelligence and Neuroscience 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/129365.
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Independent component analysis (ICA) is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes. We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy.
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Huang, Da Wei, Wu Jiao Dai, and Fei Xue Luo. "ICA Spatiotemporal Filtering Method and Its Application in GPS Deformation Monitoring." Applied Mechanics and Materials 204-208 (October 2012): 2806–12. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.2806.
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Principal component analysis (PCA) is a good method to be used in spatiotemporal filtering for regional GPS network. As an extension of PCA, independent component analysis(ICA) is also widely concerded in many fields of sciences and application researches. As a new spatiotemporal filtering method, the application of ICA in spatiotemporal filtering of the regional GPS network and GPS deformation monitoring is explored in this paper. The simulated data test shows the filtering effect of ICA is the same as PCA, both of the PCA and ICA can extract two independent components which implied in simulated common mode error. At the same time, the SCIGN data test shows the filtering effect of ICA is a litter worse than PCA, but ICA extracts not only one independent components as common mode error, it is not unique and independence that can not be provided by the PCA method. It also reflects the essence of common mode error of different station in independence. Therefore, ICA method can be applied to GPS deformation monitoring as a new spatiotemporal filtering method, the feasibility and advantage of ICA is demonstrated in the experiment of simulated data and SCIGN data.
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CHAWLA, M. P. S. "PARAMETRIZATION AND CORRECTION OF ELECTROCARDIOGRAM SIGNALS USING INDEPENDENT COMPONENT ANALYSIS." Journal of Mechanics in Medicine and Biology 07, no. 04 (December 2007): 355–79. http://dx.doi.org/10.1142/s0219519407002364.
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Electrocardiogram (ECG) signals are largely employed as a diagnostic tool in clinical practice in order to assess the cardiac status of a specimen. Independent component analysis (ICA) of measured ECG signals yields the independent sources, provided that certain requirements are fulfilled. Properly parametrized ECG signals provide a better view of the extracted ECG signals, while reducing the amount of ECG data. Independent components (ICs) of parametrized ECG signals may also be more readily interpretable than original ECG measurements or even their ICs. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG signals for a clear interpretation of ECG data in diagnostic applications. In this work, ICA is tested on the Common Standards for Electrocardiography (CSE) database files corrupted by abrupt changes, high frequency noise, power line interference, etc. The joint approximation for diagonalization of eigen matrices (JADE) algorithm for ICA is applied to three-channel ECG, and the sources are separated as ICs. In this analysis, an extension is applied to the algorithm for further correction of the extracted components. The values of R-peak before and after application of ICA are found using quadratic spline wavelet, which facilitates the estimation of the reconstruction errors. The results indicate that, in most of the cases, the percentage reconstruction error is small at around 3%. The paper also highlights the advantages, limitations, and diagnostic feature extraction capability of ICA for clinicians and medical practitioners. Kurtosis is varied in the range of 3.0–7.0, and variance of variance (Varvar) is varied in the range of 0.2–0.5.
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HORIHATA, SATOSHI, ZHONG ZHANG, TAKASHI IMAMURA, TETSUO MIYAKE, HIROSHI TODA, and YOSHIFUMI YASUDA. "BIOLOGICAL SIGNAL ANALYSIS BY INDEPENDENT COMPONENT ANALYSIS USING COMPLEX WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 04 (July 2010): 595–608. http://dx.doi.org/10.1142/s0219691310003663.
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Independent component analysis (ICA) is a useful method for blind source separation of two or more signals. We have previously proposed a new method combining ICA with the complex discrete wavelet transform (CDWT), in which voice and noise signals were separated using a new method. At that time, we used a simulated signal. In this study, we analyze measured biological signals by using a new method, and discuss its effectiveness. As an experiment, we try to separate an electromyogram (EMG) signal from an electrocardiogram (ECG) signal.
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Chawla, M. P. S. "Detection of Indeterminacies in Corrected ECG Signals Using Parameterized Multidimensional Independent Component Analysis." Computational and Mathematical Methods in Medicine 10, no. 2 (2009): 85–115. http://dx.doi.org/10.1080/17486700802193153.
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Independent component analysis (ICA) is a new technique suitable for separating independent components from electrocardiogram (ECG) complex signals. The basic idea of using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces and to decompose each rotation into elementary rotations within all two-dimensional planes spanned by the coordinate axes useful for diagnostic information of heart. In this paper, ability of ICA for parameterization of ECG signals was felt to reduce the amount of redundant ECG data. This work aims at finding an independent subspace analysis (ISA) model for ECG analysis that allows applicability to any random vectors available in an ECG data set. For the common standards for electrocardiography (CSE) based ECG data sets, joint approximate diagonalization of eigen matrices (Jade) algorithm is used to find smaller subspaces. The extracted independent components are further cleaned by statistical measures. In this study, it is also observed that the value of kurtosis coefficients for the independent components, which represents the noise component, can be further reduced using parameterized multidimensional ICA (PMICA) technique. The indeterminacies if available in the ECG data are to be analysed also using modified version of Jade algorithm to PMICA and parameterized standard ICA (PsICA) for comparative studies. The indeterminacies if available in the ECG data are reduced in PMICA better in comparison to the analysis done using PsICA. The simulation results obtained indicate that ICA definitely improves signal–noise ratio (SNR) like the other higher order digital filtering methods like Kalman, Butterworth etc. with minimum reconstruction errors. Here, it is also confirmed that re-parameterization of the standard ICA model results into a ‘component model’ using MICA technique, which is geometric in spirit and free of indeterminacies existing in sICA model.
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Abbas, Nidaa, and Hussein Salman. "Enhancing Linear Independent Component Analysis: Comparison of Various Metaheuristic Methods." Iraqi Journal for Electrical and Electronic Engineering 16, no. 1 (June 7, 2020): 113–22. http://dx.doi.org/10.37917/ijeee.16.1.14.
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Various methods have been exploited in the blind source separation problems, especially in cocktail party problems. The most commonly used method is the independent component analysis (ICA). Many linear and nonlinear ICA methods, such as the radial basis functions (RBF) and self-organizing map (SOM) methods utilise neural networks and genetic algorithms as optimisation methods. For the contrast function, most of the traditional methods, especially the neural networks, use the gradient descent as an objective function for the ICA method. Most of these methods trap in local minima and consume numerous computation requirements. Three metaheuristic optimisation methods, namely particle, quantum particle, and glowworm swarm optimisation methods are introduced in this study to enhance the existing ICA methods. The proposed methods exhibit better results in separation than those in the traditional methods according to the following separation quality measurements: signal-to-noise ratio, signal-to-interference ratio, log-likelihood ratio, perceptual evaluation speech quality and computation time. These methods effectively achieved an independent identical distribution condition when the sampling frequency of the signals is 8 kHz.
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Li, Wenhao, Fei Li, Shengkai Zhang, Jintao Lei, Qingchuan Zhang, and Lexian Yuan. "Spatiotemporal Filtering and Noise Analysis for Regional GNSS Network in Antarctica Using Independent Component Analysis." Remote Sensing 11, no. 4 (February 14, 2019): 386. http://dx.doi.org/10.3390/rs11040386.
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The common mode error (CME) and optimal noise model are the two most important factors affecting the accuracy of time series in regional Global Navigation Satellite System (GNSS) networks. Removing the CME and selecting the optimal noise model can effectively improve the accuracy of GNSS coordinate time series. The CME, a major source of error, is related to the spatiotemporal distribution; hence, its detrimental effects on time series can be effectively reduced through spatial filtering. Independent component analysis (ICA) is used to filter the time series recorded by 79 GPS stations in Antarctica from 2010 to 2018. After removing stations exhibiting strong local effects using their spatial responses, the filtering results of residual time series derived from principal component analysis (PCA) and ICA are compared and analyzed. The Akaike information criterion (AIC) is then used to determine the optimal noise model of the GPS time series before and after ICA/PCA filtering. The results show that ICA is superior to PCA regarding both the filter results and the consistency of the optimal noise model. In terms of the filtering results, ICA can extract multisource error signals. After ICA filtering, the root mean square (RMS) values of the residual time series are reduced by 14.45%, 8.97%, and 13.27% in the east (E), north (N), and vertical (U) components, respectively, and the associated speed uncertainties are reduced by 13.50%, 8.06% and 11.82%, respectively. Furthermore, different GNSS time series in Antarctica have different optimal noise models with different noise characteristics in different components. The main noise models are the white noise plus flicker noise (WN+FN) and white noise plus power law noise (WN+PN) models. Additionally, the spectrum index of most PN is close to that of FN. Finally, there are more stations with consistent optimal noise models after ICA filtering than there are after PCA filtering.
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CHAWLA, M. P. S. "MULTIDIMENSIONAL INDEPENDENT COMPONENT ANALYSIS FOR STATISTICAL ESTIMATIONS OF INDETERMINACIES IN ELECTROCARDIOGRAMS." Journal of Mechanics in Medicine and Biology 09, no. 03 (September 2009): 345–75. http://dx.doi.org/10.1142/s0219519409002997.
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Independent component analysis (ICA) is a technique capable of separating independent components (ICs) from complex electrocardiogram (ECG) signals. The basic intention behind using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces. This study highlights the ability of ICA for parametrization of ECG signals to reduce the amount of redundant ECG data if any in a data set. The aim of this paper is to justify the underlying theory of the use of ICA and how it can be extended to for MICA separation of the ECG signals for combinational leads to attain most useful diagnostic information, which was not discussed in other some similar previous publications in this field. It is also investigated that the value of kurtosis coefficients for the ICs, which represents the noise component, can be further reduced using parametrized multidimensional independent component analysis (PMICA) technique. The indeterminacies available in the ECG data are also analyzed using modified version of Jade algorithm for PMICA and parametrized standard independent component analysis (PSICA). For the ECG data set, Jade algorithm is applied first to find smaller subspaces for MICA analysis and can therefore be regarded as a basis algorithm for PMICA analysis. The simulation results are obtained in Matlab environment to indicate that, ICA can definitely improve signal–noise ratio (SNR) in minimizing the reconstruction errors. The future scope of MICA expected by author is that, by reconsidering the notion of ICA, a more general perspective can be envisioned: i.e. modified multidimensional independent component analysis (MMICA). It would be based on a morphological geometric parametrization (MGP) which would further reduce the indeterminacies involved in matrix-based modeling (MBM).
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Ciaramella, A., E. De Lauro, S. De Martino, B. Di Lieto, M. Falanga, and R. Tagliaferri. "Characterization of Strombolian events by using independent component analysis." Nonlinear Processes in Geophysics 11, no. 4 (October 21, 2004): 453–61. http://dx.doi.org/10.5194/npg-11-453-2004.
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Abstract. We apply Independent Component Analysis (ICA) to seismic signals recorded at Stromboli volcano. Firstly, we show how ICA works considering synthetic signals, which are generated by dynamical systems. We prove that Strombolian signals, both tremor and explosions, in the high frequency band (>0.5 Hz), are similar in time domain. This seems to give some insights to the organ pipe model generation for the source of these events. Moreover, we are able to recognize in the tremor signals a low frequency component (<0.5 Hz), with a well defined peak corresponding to 30s.
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Chawla, M. P. S. "Parameterization and R-Peak Error Estimations of ECG Signals Using Independent Component Analysis." Computational and Mathematical Methods in Medicine 8, no. 4 (2007): 263–85. http://dx.doi.org/10.1080/17486700701776348.
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Principal component analysis (PCA) is used to reduce dimensionality of electrocardiogram (ECG) data prior to performing independent component analysis (ICA). A newly developed PCA variance estimator by the author has been applied for detecting true, actual and false peaks of ECG data files. In this paper, it is felt that the ability of ICA is also checked for parameterization of ECG signals, which is necessary at times. Independent components (ICs) of properly parameterized ECG signals are more readily interpretable than the measurements themselves, or their ICs. The original ECG recordings and the samples are corrected by statistical measures to estimate the noise statistics of ECG signals and find the reconstruction errors. The capability of ICA is justified by finding the true, false and actual peaks of around 25–50, CSE (common standards for electrocardiography) database ECG files. In the present work, joint approximation for diagonalization of the eigen matrices (Jade) algorithm is applied to 3-channel ECG. ICA processing of different cases is dealt with and the R-peak magnitudes of the ECG waveforms before and after applying ICA are found and marked. ICA results obtained indicate that in most of the cases, the percentage error in reconstruction is very small. The developed PCA variance estimator along with the quadratic spline wavelet gave a sensitivity of 97.47% before applying ICA and 98.07% after ICA processing.
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Jäckel, David, Urs Frey, Michele Fiscella, Felix Franke, and Andreas Hierlemann. "Applicability of independent component analysis on high-density microelectrode array recordings." Journal of Neurophysiology 108, no. 1 (July 1, 2012): 334–48. http://dx.doi.org/10.1152/jn.01106.2011.
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Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as “spike sorting.” For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.
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AKROUT, ALI, DHOUHA TOUNSI, MOHAMED TAKTAK, MOHAMED SLIM ABBÈS, and MOHAMED HADDAR. "ESTIMATION OF DYNAMIC SYSTEM'S EXCITATION FORCES BY THE INDEPENDENT COMPONENT ANALYSIS." International Journal of Applied Mechanics 04, no. 03 (September 2012): 1250032. http://dx.doi.org/10.1142/s1758825112500329.
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This paper deals with a numerical investigation for the estimation of dynamic system's excitation sources using the independent component analysis (ICA). In fact, the ICA concept is an important technique of the blind source separation (BSS) method. In this case, only the dynamic responses of a given mechanical system are supposed to be known. Thus, the main difficulty of such problem resides in the existence of any information about the excitation forces. For this purpose, the ICA concept, which consists on optimizing a fourth-order statistical criterion, can be highlighted. Hence, a numerical procedure based on the signal sources independency in the ICA concept is developed. In this work, the analytical or the finite element (FE) dynamic responses are calculated and exploited in order to identify the excitation forces applied on discrete (mass-spring) and continuous (beam) systems. Then, estimated results obtained by the ICA concept are presented and compared to those achieved analytically or by the FE and the modal recombination methods. Since a good agreement is obtained, this approach can be used when the vibratory responses of a dynamic system are obtained through sensor's measurements.
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Supriya, P., and T. N. Padmanabhan Nambiar. "Noise Based Independent Component Analysis Model for Harmonic Current Estimation." Advanced Materials Research 433-440 (January 2012): 2551–55. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2551.
Full textAbstract:
In a deregulating environment, Independent Component Analysis (ICA) is used to estimate the harmonic currents of non linear loads as it does not require information about the topology of the network. However, analysis is done by ignoring the effect of various noises that creep into the measurement system. In the present work, the effect of environmental noise on a simple interconnected power system with five buses is taken up. The two algorithms namely Fast ICA (FICA) and Efficient Variant Fast ICA(EFICA) are used for the analysis. A fixed noise is added and it is eliminated using whitening technique The simulation results of both algorithms show that noise elimination by whitening technique is highly successful. However, EFICA gives better results than FICA when random fluctuations of load exist rather than when fixed variations exist.
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Obradovic, D., and G. Deco. "Information Maximization and Independent Component Analysis: Is There a Difference?" Neural Computation 10, no. 8 (November 1, 1998): 2085–101. http://dx.doi.org/10.1162/089976698300016972.
Full textAbstract:
This article provides a detailed and rigorous analysis of the two commonly used methods for redundancy reduction: linear independent component analysis (ICA) posed as a direct minimization of a suitably chosen redundancy measure and information maximization (InfoMax) of a continuous stochastic signal transmitted through an appropriate nonlinear network. The article shows analytically that ICA based on the Kullback-Leibler information as a redundancy measure and InfoMax lead to the same solution if the parameterization of the output nonlinear functions in the latter method is sufficiently rich. Furthermore, this work discusses the alternative redundancy measures not based on the Kullback-Leibler information distance. The practical issues of applying ICA and InfoMax are also discussed and illustrated on the problem of extracting statistically independent factors from a linear, pixel-by-pixel mixture of images.
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CONG, FENGYU, IGOR KALYAKIN, TIINA HUTTUNEN-SCOTT, HONG LI, HEIKKI LYYTINEN, and TAPANI RISTANIEMI. "SINGLE-TRIAL BASED INDEPENDENT COMPONENT ANALYSIS ON MISMATCH NEGATIVITY IN CHILDREN." International Journal of Neural Systems 20, no. 04 (August 2010): 279–92. http://dx.doi.org/10.1142/s0129065710002413.
Full textAbstract:
Independent component analysis (ICA) does not follow the superposition rule. This motivates us to study a negative event-related potential — mismatch negativity (MMN) estimated by the single-trial based ICA (sICA) and averaged trace based ICA (aICA), respectively. To sICA, an optimal digital filter (ODF) was used to remove low-frequency noise. As a result, this study demonstrates that the performance of the sICA+ODF and aICA could be different. Moreover, MMN under sICA+ODF fits better with the theoretical expectation, i.e., larger deviant elicits larger MMN peak amplitude.