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Artículos de revistas sobre el tema "Independent component analysis"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 20 de febrero de 2023

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1

Kemp, Freda. "Independent Component Analysis Independent Component Analysis: Principles and Practice". Journal of the Royal Statistical Society: Series D (The Statistician) 52, n.º 3 (octubre de 2003): 412. http://dx.doi.org/10.1111/1467-9884.00369_14.

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2

Hong, Sung Ee. "Exploring Independent Component Analysis Based on Ball Covariance". Korean Data Analysis Society 21, n.º 6 (31 de diciembre de 2019): 2721–35. http://dx.doi.org/10.37727/jkdas.2019.21.6.2721.

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3

Unnisa, Yaseen, Danh Tran y Fu Chun Huang. "Statistical Independence and Independent Component Analysis". Applied Mechanics and Materials 553 (mayo de 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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4

KAWAMOTO, Mitsuru. "Independent Component Analysis". Journal of Japan Society for Fuzzy Theory and Systems 11, n.º 5 (1999): 759–62. http://dx.doi.org/10.3156/jfuzzy.11.5_55.

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5

Sztemberg-Lewandowska, Mirosława. "INDEPENDENT COMPONENT ANALYSIS". Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, n.º 468 (2017): 222–29. http://dx.doi.org/10.15611/pn.2017.468.23.

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6

Fearn, Tom. "Independent Component Analysis". NIR news 19, n.º 3 (mayo de 2008): 13–14. http://dx.doi.org/10.1255/nirn.1073.

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7

Liu, Thomas T., Karla L. Miller, Eric C. Wong, Lawrence R. Frank y Richard B. Buxton. "Identifying meaningful components in independent component analysis". NeuroImage 11, n.º 5 (mayo de 2000): S652. http://dx.doi.org/10.1016/s1053-8119(00)91582-9.

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8

Hyvärinen, Aapo, Patrik O. Hoyer y Mika Inki. "Topographic Independent Component Analysis". Neural Computation 13, n.º 7 (1 de julio de 2001): 1527–58. http://dx.doi.org/10.1162/089976601750264992.

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In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated “independent” components are often not at all independent. We propose that this residual dependence structure could be used to define a topo-graphic order for the components. In particular, a distance between two components could be defined using their higher-order correlations, and this distance could be used to create a topographic representation. Thus, we obtain a linear decomposition into approximately independent components, where the dependence of two components is approximated by the proximity of the components in the topographic representation.

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9

Leite, I. C. C., T. Sáfadi y M. L. M. Carvalho. "Evaluation of seed radiographic images by independent component analysis and discriminant analysis". Seed Science and Technology 41, n.º 2 (1 de agosto de 2013): 235–44. http://dx.doi.org/10.15258/sst.2013.41.2.06.

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10

Honório, Bruno César Zanardo, Alexandre Cruz Sanchetta, Emilson Pereira Leite y Alexandre Campane Vidal. "Independent component spectral analysis". Interpretation 2, n.º 1 (1 de febrero de 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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11

Samarov, Alexander y Alexandre Tsybakov. "Nonparametric independent component analysis". Bernoulli 10, n.º 4 (agosto de 2004): 565–82. http://dx.doi.org/10.3150/bj/1093265630.

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12

Kawaguchi, Atsushi y Young K. Truong. "LOGSPLINE INDEPENDENT COMPONENT ANALYSIS". Bulletin of informatics and cybernetics 43 (diciembre de 2011): 83–94. http://dx.doi.org/10.5109/1434313.

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13

Jie Luo, Bo Hu, Xie-Ting Ling y Ruey-Wen Liu. "Principal independent component analysis". IEEE Transactions on Neural Networks 10, n.º 4 (julio de 1999): 912–17. http://dx.doi.org/10.1109/72.774259.

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14

Chen, Aiyou y Peter J. Bickel. "Efficient independent component analysis". Annals of Statistics 34, n.º 6 (diciembre de 2006): 2825–55. http://dx.doi.org/10.1214/009053606000000939.

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15

Dhir, C. S. y Soo-Young Lee. "Discriminant Independent Component Analysis". IEEE Transactions on Neural Networks 22, n.º 6 (junio de 2011): 845–57. http://dx.doi.org/10.1109/tnn.2011.2122266.

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16

Singer, A. "Spectral independent component analysis". Applied and Computational Harmonic Analysis 21, n.º 1 (julio de 2006): 135–44. http://dx.doi.org/10.1016/j.acha.2006.03.003.

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17

Jing, Shuangxi, Qi Liu, Chenxu Luo y Penghui Shi. "Comparison study of fast independent component analysis and constrained independent component analysis". Vibroengineering PROCEDIA 20 (19 de octubre de 2018): 57–63. http://dx.doi.org/10.21595/vp.2018.20089.

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18

Hyvärinen, Aapo. "Independent component analysis: recent advances". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, n.º 1984 (13 de febrero de 2013): 20110534. http://dx.doi.org/10.1098/rsta.2011.0534.

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Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some recent developments in the theory since the year 2000. The main topics are: analysis of causal relations, testing independent components, analysing multiple datasets (three-way data), modelling dependencies between the components and improved methods for estimating the basic model.

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19

Miettinen, Jari, Markus Matilainen, Klaus Nordhausen y Sara Taskinen. "Extracting Conditionally Heteroskedastic Components using Independent Component Analysis". Journal of Time Series Analysis 41, n.º 2 (8 de septiembre de 2019): 293–311. http://dx.doi.org/10.1111/jtsa.12505.

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20

JENTZSCH, INES. "INDEPENDENT COMPONENT ANALYSIS SEPARATES SEQUENCE-SENSITIVE ERP COMPONENTS". International Journal of Bifurcation and Chaos 14, n.º 02 (febrero de 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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21

Suzuki, Taiji y Masashi Sugiyama. "Least-Squares Independent Component Analysis". Neural Computation 23, n.º 1 (enero de 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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22

Saju, S. y G. Thirugnanam. "Performance Analysis of Image Watermarking Using Contourlet Transform and Extraction Using Independent Component Analysis". Journal of Computers 11, n.º 3 (mayo de 2016): 258–65. http://dx.doi.org/10.17706/jcp.11.3.258-265.

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23

Fouda, Mohammed E., Emre Neftci, Ahmed Eltawil y Fadi Kurdahi. "Independent Component Analysis Using RRAMs". IEEE Transactions on Nanotechnology 18 (2019): 611–15. http://dx.doi.org/10.1109/tnano.2018.2880734.

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24

Gepshtein, Shai y Yosi Keller. "Iterative spectral independent component analysis". Signal Processing 155 (febrero de 2019): 368–76. http://dx.doi.org/10.1016/j.sigpro.2018.07.029.

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25

Stone, James V. "Independent component analysis: an introduction". Trends in Cognitive Sciences 6, n.º 2 (febrero de 2002): 59–64. http://dx.doi.org/10.1016/s1364-6613(00)01813-1.

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26

Oja, Erkki, Stefan Harmeling y Luis Almeida. "Independent component analysis and beyond". Signal Processing 84, n.º 2 (febrero de 2004): 215–16. http://dx.doi.org/10.1016/j.sigpro.2003.11.005.

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27

Barbedor, Pascal. "Independent component analysis by wavelets". TEST 18, n.º 1 (14 de julio de 2007): 136–55. http://dx.doi.org/10.1007/s11749-007-0073-7.

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28

Ma, Libo y Liqing Zhang. "Overcomplete topographic independent component analysis". Neurocomputing 71, n.º 10-12 (junio de 2008): 2217–23. http://dx.doi.org/10.1016/j.neucom.2007.06.013.

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29

Knaak, Mirko, Shoko Araki y Shoji Makino. "Geometrically Constrained Independent Component Analysis". IEEE Transactions on Audio, Speech and Language Processing 15, n.º 2 (febrero de 2007): 715–26. http://dx.doi.org/10.1109/tasl.2006.876730.

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30

Ranjith, Jayasanthi y N. J. R. Muniraj. "High Performance Independent Component Analysis". Asian Journal of Scientific Research 7, n.º 4 (15 de septiembre de 2014): 460–71. http://dx.doi.org/10.3923/ajsr.2014.460.471.

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31

Bonhomme, Stéphane y Jean-Marc Robin. "Consistent noisy independent component analysis". Journal of Econometrics 149, n.º 1 (abril de 2009): 12–25. http://dx.doi.org/10.1016/j.jeconom.2008.12.019.

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32

HYVÄRINEN, AAPO y ERKKI OJA. "SIMPLE NEURON MODELS FOR INDEPENDENT COMPONENT ANALYSIS". International Journal of Neural Systems 07, n.º 06 (diciembre de 1996): 671–87. http://dx.doi.org/10.1142/s0129065796000646.

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Recently, several neural algorithms have been introduced for Independent Component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbian-like learning rules are introduced for estimating one of the independent components from sphered data. Some of the learning rules can be used to estimate an independent component which has a negative kurtosis, and the others estimate a component of positive kurtosis. Next, a two-unit system is introduced to estimate an independent component of any kurtosis. The results are then generalized to estimate independent components from non-sphered (raw) mixtures. To separate several independent components, a system of several neurons with linear negative feedback is used. The convergence of the learning rules is rigorously proven without any unnecessary hypotheses on the distributions of the independent components.

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33

NiketBorade, Sushma y Ratnadeep R. Deshmukh. "Comparative Study of Principal Component Analysis and Independent Component Analysis". International Journal of Computer Applications 92, n.º 15 (18 de abril de 2014): 45–49. http://dx.doi.org/10.5120/16087-5399.

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34

Khokher, Rohit, Ram Chandra Singh y Rahul Kumar. "Footprint Recognition with Principal Component Analysis and Independent Component Analysis". Macromolecular Symposia 347, n.º 1 (enero de 2015): 16–26. http://dx.doi.org/10.1002/masy.201400045.

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35

Wang, Jing Hui y Shu Gang Tang. "Quadratic Independent Component Analysis Based on Sparse Component". Applied Mechanics and Materials 442 (octubre de 2013): 562–67. http://dx.doi.org/10.4028/www.scientific.net/amm.442.562.

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In this paper, a novel signal blind separation using adaptive multi-resolution independent component analysis based on sparse component is presented. This method separates mixed signal based on quadratic function and sparse representation. The quadratic function can be interpreted as the time-frequency function or time-scale function, or other. The sparse expression is the original signal through the dictionary to get their coefficients. Most of the coefficients is very small, close to zero, can greatly save separate computing time. At the same time this method can filter out the noise. The argorithm extends the separate technology from time-frequency domain to sparse mutil-resolution domain. The experimental result showed the method can be effective separation of mixed signals. And it shows that the method is feasible.

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36

Wang, Jing. "Mixed principal-component-analysis/independent-component-analysis transform for hyperspectral image analysis". Optical Engineering 46, n.º 7 (1 de julio de 2007): 077006. http://dx.doi.org/10.1117/1.2759225.

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37

Feng, Pingxing y Liping Li. "On extending the Noisy Independent Component Analysis to Impulsive Components". Wireless Personal Communications 88, n.º 3 (28 de noviembre de 2015): 415–27. http://dx.doi.org/10.1007/s11277-015-3135-2.

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38

Seok, Jong-Won. "Audio Watermarking Using Independent Component Analysis". Journal of information and communication convergence engineering 10, n.º 2 (30 de junio de 2012): 175–80. http://dx.doi.org/10.6109/jicce.2012.10.2.175.

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39

SHU, Lang, Qin SHU y Jing SU. "Independent component analysis with innovation model". Journal of Computer Applications 31, n.º 2 (7 de abril de 2011): 556–58. http://dx.doi.org/10.3724/sp.j.1087.2011.00556.

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40

Yokote, Ryota y Yasuo Matsuyama. "Rapid Algorithm for Independent Component Analysis". Journal of Signal and Information Processing 03, n.º 03 (2012): 275–85. http://dx.doi.org/10.4236/jsip.2012.33037.

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41

Ibrahim, Wan Nurhidayah, Mohd Syahid Anuar, Ali Selamat y Ondrej Krejcar. "BOTNET DETECTION USING INDEPENDENT COMPONENT ANALYSIS". IIUM Engineering Journal 23, n.º 1 (4 de enero de 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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42

Bartlett, M. S., J. R. Movellan y T. J. Sejnowski. "Face recognition by independent component analysis". IEEE Transactions on Neural Networks 13, n.º 6 (noviembre de 2002): 1450–64. http://dx.doi.org/10.1109/tnn.2002.804287.

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43

Plumbley, M. D. "Algorithms for nonnegative independent component analysis". IEEE Transactions on Neural Networks 14, n.º 3 (mayo de 2003): 534–43. http://dx.doi.org/10.1109/tnn.2003.810616.

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44

James, Christopher J. y Christian W. Hesse. "Independent component analysis for biomedical signals". Physiological Measurement 26, n.º 1 (21 de diciembre de 2004): R15—R39. http://dx.doi.org/10.1088/0967-3334/26/1/r02.

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45

Xie, Xiaobo, Fatih Yaman, Xiang Zhou y Guifang Li. "Polarization Demultiplexing by Independent Component Analysis". IEEE Photonics Technology Letters 22, n.º 11 (junio de 2010): 805–7. http://dx.doi.org/10.1109/lpt.2010.2046158.

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46

Pang, X. y S. Y. Lee. "Independent component analysis for beam measurements". Journal of Applied Physics 106, n.º 7 (octubre de 2009): 074902. http://dx.doi.org/10.1063/1.3226858.

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47

Rayens, William S. "Independent Component Analysis: Principles and Practice". Technometrics 45, n.º 1 (febrero de 2003): 107–8. http://dx.doi.org/10.1198/tech.2003.s26.

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48

Grimes, Carrie. "Independent Component Analysis: Principles and Practice". Journal of the American Statistical Association 97, n.º 460 (diciembre de 2002): 1214–15. http://dx.doi.org/10.1198/jasa.2002.s247.

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49

Shen, Weining, Jing Ning y Ying Yuan. "Rate-adaptive Bayesian independent component analysis". Electronic Journal of Statistics 10, n.º 2 (2016): 3247–64. http://dx.doi.org/10.1214/16-ejs1183.

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50

Jiann-Ming Wu y Shih-Jang Chiu. "Independent component analysis using Potts models". IEEE Transactions on Neural Networks 12, n.º 2 (marzo de 2001): 202–11. http://dx.doi.org/10.1109/72.914518.

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