Tesis sobre el tema "Independent component analysis"
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Gao, Pei. "Nonlinear independent component analysis". Thesis, University of Newcastle Upon Tyne, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437979.
Texto completoHarmeling, Stefan. "Independent component analysis and beyond". Phd thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973631805.
Texto completoBlaschke, Tobias. "Independent component analysis and slow feature analysis". Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2005. http://dx.doi.org/10.18452/15270.
Texto completoWithin this thesis, we focus on the relation between independent component analysis (ICA) and slow feature analysis (SFA). To allow a comparison between both methods we introduce CuBICA2, an ICA algorithm based on second-order statistics only, i.e.\ cross-correlations. In contrast to algorithms based on higher-order statistics not only instantaneous cross-correlations but also time-delayed cross correlations are considered for minimization. CuBICA2 requires signal components with auto-correlation like in SFA, and has the ability to separate source signal components that have a Gaussian distribution. Furthermore, we derive an alternative formulation of the SFA objective function and compare it with that of CuBICA2. In the case of a linear mixture the two methods are equivalent if a single time delay is taken into account. The comparison can not be extended to the case of several time delays. For ICA a straightforward extension can be derived, but a similar extension to SFA yields an objective function that can not be interpreted in the sense of SFA. However, a useful extension in the sense of SFA to more than one time delay can be derived. This extended SFA reveals the close connection between the slowness objective of SFA and temporal predictability. Furthermore, we combine CuBICA2 and SFA. The result can be interpreted from two perspectives. From the ICA point of view the combination leads to an algorithm that solves the nonlinear blind source separation problem. From the SFA point of view the combination of ICA and SFA is an extension to SFA in terms of statistical independence. Standard SFA extracts slowly varying signal components that are uncorrelated meaning they are statistically independent up to second-order. The integration of ICA leads to signal components that are more or less statistically independent.
Brock, James L. "Acoustic classification using independent component analysis /". Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/2067.
Texto completoPapathanassiou, Christos. "Independent component analysis of magnetoencephalographic signals". Thesis, University of Surrey, 2003. http://epubs.surrey.ac.uk/771941/.
Texto completoMiskin, James William. "Ensemble learning for independent component analysis". Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621116.
Texto completoGarvey, Jennie Hill. "Independent component analysis by entropy maximization (infomax)". Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FGarvey.pdf.
Texto completoThesis Advisor(s): Frank E. Kragh. "June 2007." Includes bibliographical references (p. 103). Also available in print.
Mitianoudis, Nikolaos. "Audio source separation using independent component analysis". Thesis, Queen Mary, University of London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406171.
Texto completoChoudrey, Rizwan A. "Variational methods for Bayesian independent component analysis". Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275566.
Texto completoKalkan, Olcay Altınkaya Mustafa Aziz. "Independent component analysis applications in CDMA systems/". [s.l.]: [s.n.], 2004. http://library.iyte.edu.tr/tezler/master/elektronikvehaberlesme/T000473.rar.
Texto completoBjörling, Robin. "Denoising of Infrared Images Using Independent Component Analysis". Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4954.
Texto completoDenna uppsats syftar till att undersöka användbarheten av metoden Independent Component Analysis (ICA) för brusreducering av bilder tagna av infraröda kameror. Speciellt fokus ligger på att reducera additivt brus. Bruset delas upp i två delar, det Gaussiska bruset samt det sensorspecifika mönsterbruset. För att reducera det Gaussiska bruset används en populär metod kallad sparse code shrinkage som bygger på ICA. En ny metod, även den byggandes på ICA, utvecklas för att reducera mönsterbrus. För varje sensor utförs, i den nya metoden, en analys av bilddata för att manuellt identifiera typiska mönsterbruskomponenter. Dessa komponenter används därefter för att reducera mönsterbruset i bilder tagna av den aktuella sensorn. Det visas att metoderna ger goda resultat på infraröda bilder. Algoritmerna testas både på syntetiska såväl som på verkliga bilder och resultat presenteras och jämförs med andra algoritmer.
The purpose of this thesis is to evaluate the applicability of the method Independent Component Analysis (ICA) for noise reduction of infrared images. The focus lies on reducing the additive uncorrelated noise and the sensor specific additive Fixed Pattern Noise (FPN). The well known method sparse code shrinkage, in combination with ICA, is applied to reduce the uncorrelated noise degrading infrared images. The result is compared to an adaptive Wiener filter. A novel method, also based on ICA, for reducing FPN is developed. An independent component analysis is made on images from an infrared sensor and typical fixed pattern noise components are manually identified. The identified components are used to fast and effectively reduce the FPN in images taken by the specific sensor. It is shown that both the FPN reduction algorithm and the sparse code shrinkage method work well for infrared images. The algorithms are tested on synthetic as well as on real images and the performance is measured.
Sahambi, Harkirat S. "Appearance based object recognition using independent component analysis". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0017/MQ54320.pdf.
Texto completoWu, Hao-cun. "Independent component analysis and its applications in finance". Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39559099.
Texto completoBeckmann, Christian F. "Independent component analysis for functional magnetic resonance imaging". Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404108.
Texto completo吳浩存 y Hao-cun Wu. "Independent component analysis and its applications in finance". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39559099.
Texto completoRobila, Stefan Alexandru. "Independent component analysis based feature extraction for hyperspectral images". Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.
Texto completoBaylor, Martha-Elizabeth. "Analog optoelectronic independent component analysis for radio frequency signals". Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3288865.
Texto completoE, Okwelume Gozie y Ezeude Anayo Kingsley. "BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS". Thesis, Blekinge Tekniska Högskola, Avdelningen för signalbehandling, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1312.
Texto completoGozie: modebelu2001@yahoo.com Anayo: ezeudea@yahoo.com
Herrmann, Frank. "Independent component analysis with applications to blind source separation". Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399147.
Texto completoRezaee, Sayed Majid. "A study on independent component analysis over galois fields". reponame:Repositório Institucional da UnB, 2015. http://dx.doi.org/10.26512/2015.12.D.20421.
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Nas últimas décadas, o problema de separação cega de fontes (BSS, do inglês Blind Source Separation) – que trata de estimar um conjunto desconhecido de fontes de sinais a partir de versões misturadas destes – tornou-se relevante em vários campos da engenharia, incluindo o processamento matricial, comunicações sem fio, processamento de sinais médicos, processamento de voz e engenharia biomédica. A fim de resolver o problema de BSS no contexto de modelos lineares, considerando-se várias técnicas possíveis, a Análise de Componentes Independentes (ICA, do inglês Independent Component Analysis) – que utiliza a independência estatística das fontes como uma premissa – demonstrou ser uma das mais importantes estratégias de solução. Além disso, embora o modelo de BSS/ICA para sinais reais ou complexos esteja bem estabelecido, a recente perspectiva de uma formulação do problema com sinais e modelos definidos em corpos de Galois oferece várias possibilidades de análise e contribuições. Esta dissertação de mestrado realiza um estudo da Análise de Componentes Independentes em corpos de Galois, considerando os conceitos teóricos e abordagens para o problema, assim como dos algoritmos estado-da-arte até agora propostos, em termos de suas capacidades de separação e custo computacional. Especificamente, as técnicas dos algoritmos AMERICA e MEXICO são estudadas juntamente com o algoritmo cobICA. Como as simulações experimentais indicam, devido à sua complexidade computacional menor e uma qualidade de desempenho satisfatório, o algoritmo cobICA apresenta-se como uma solução de compromisso entre os algoritmos AMERICA e MEXICO para executar BSS/ICA em corpos de Galois.
Over the past decades, the Blind Source Separation (BSS) problem – which deals with estimating an unknown set of source signals from their measured mixtures –has become prevalent in several engineering fields, including array processing, wireless communications, medical signal processing, speech processing and biomedical engineering. In order to solve the BSS problem in the context of linear models, considering several possible techniques, Independent Component Analysis (ICA) – which uses statistical independence of the source signals as a premise – has been shown to be one of the most important approaches. Furthermore, although the BSS/ICA framework for real- or complex-valued signals is firmly established, the recent perspective of a BSS/ICA formulation where the signals and models are defined over Galois fields gives several possibilities of analyzes and contributions. This Master’s thesis performs a study on Independent Component Analysis over Galois fields, considering the theoretical concepts and aspects of the problem and the investigation, in terms of capability and efficiency, of the state-of-the-art algorithms so far introduced. In this context, AMERICA and MEXICO techniques are studied, along with cobICA algorithm – a bioinspired framework based on cob-aiNet[C] immune-inspired algorithm –, mainly focusing on comparing the quality of separation and on discussing the computational burden of each technique. As the experimental simulations indicate, due to its lower computational complexity and a satisfactory performance quality, cobICA takes place as a compromise solution between AMERICA and MEXICO algorithms, to perform BSS/ICA over Galois fields.
Shawli, Alaa. "Scoring the SF-36 health survey in scleroderma using independent component analysis and principle component analysis". Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97180.
Texto completoLa version abrégée du questionnaire SF-36 est largement utilisée pour valider la qualité de vie reliée à la santé. Ce questionnaire fournit huit scores s'attardant à la capacité fonctionnelle et au bien-être, lesquels sont regroupés en cotes sommaires attribuées aux composantes physiques et mentales. Cependant, des études récentes ont rapporté des résultats contradictoires entre les huit sous-échelles et les deux cotes sommaires lorsque les scores sont obtenus auprès de sujets malades. Cette discordance serait due à la méthode utilisée pour calculer les cotes sommaires du SF-36 qui est fondée sur l'analyse en composantes principales avec rotation orthogonale.Dans cette thèse, nous explorons diverses méthodes dans le but d'identifier une méthode plus précise pour calculer les cotes sommaires du SF-36 attribuées aux composantes physiques et mentales (CCP et CCM), en mettant l'accent sur des sous-populations de sujets malades. Nous évaluerons d'abord des méthodes traditionnelles d'analyse de données, telles que l'analyse en composantes principales (ACP) et l'analyse factorielle, en utilisant l'étude de l'estimation du maximum de vraisemblance et en appliquant les rotations orthogonale et oblique aux deux méthodes sur les données du registre du Groupe de recherche canadien sur la sclérodermie. Nous comparons ces approches courantes à une méthode d'analyse de données développée récemment à partir de travaux de recherche sur le réseau neuronal et le traitement du signal, l'analyse en composantes indépendantes (ACI).Nous avons découvert que la rotation oblique est la seule méthode qui réduit les cotes attribuées aux composantes mentales moyennes afin de mieux les corréler aux scores de la sous-échelle des symptômes mentaux. Dans le but de mieux comprendre les différences entre la rotation orthogonale et la rotation oblique, nous avons étudié le rendement de l'ACP avec deux approches pour déterminer les véritables cotes sommaires attribuées aux composantes physiques et mentales dans une population simulée de sujets malades pour laquelle les données étaient connues. Nous avons exploré les méthodes dans des situations où les scores véritables étaient indépendants et lorsqu'ils étaient corrélés. Nous avons conclu que le rendement de l'ACI et de l'ACP associées à la rotation orthogonale était très similaire lorsque les données étaient indépendantes, mais que le rendement différait lorsque les données étaient corrélées (ACI étant moins performante). L'ACP associée à la rotation oblique a tendance à être moins performante que les deux méthodes lorsque les données étaient indépendantes, mais elle est plus performante lorsque les données étaient corrélées. Nous discutons également du lien entre l'ACI et l'ACP avec la rotation orthogonale, ce qui appuie l'emploi de la rotation varimax dans le questionnaire SF 36.Enfin, nous avons appliqué l'ACI aux données sur la sclérodermie et nous avons mis en évidence une corrélation relativement faible entre l'ACI et l'ACP sans rotation dans l'estimation des scores CCP et CCM, et une corrélation très élevée entre l'ACI et l'ACP avec rotation varimax. L'ACP avec rotation oblique présentait également une corrélation relativement élevée avec l'ACI. Par conséquent, nous en avons conclu que l'ACI pourrait servir de solution de compromis entre ces deux méthodes.
Sohr, Mandy. "Analysis of functional magnetic resonance imaging time series by independent component analysis". [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=985601965.
Texto completoAfsari, Bijan. "Gradient flow based matrix joint diagonalization for independent component analysis". College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1352.
Texto completoThesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Elsabrouty, Maha. "Riemannian geometry based blind signal separation using independent component analysis". Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/29291.
Texto completoSarfraz, M. "Role of independent component analysis in intelligent ECG signal processing". Thesis, University of Salford, 2014. http://usir.salford.ac.uk/33200/.
Texto completoZakeri, Zohreh. "Optimised use of independent component analysis for EEG signal processing". Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7430/.
Texto completoWang, Suogang. "Enhancing brain-computer interfacing through advanced independent component analysis techniques". Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/65897/.
Texto completoVigon, Laurence Celine. "Independent component analysis techniques and their performance evaluation for electroencephalography". Thesis, Sheffield Hallam University, 2002. http://shura.shu.ac.uk/20479/.
Texto completoKent, Michael. "The value of independent component analysis in identifying climate processes". Master's thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/11457.
Texto completoJohnson, Robert Spencer. "Incorporation of prior information into independent component analysis of FMRI". Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711637.
Texto completoWang, Fei. "Vertical beam emittance correction with independent component analysis measurement method". [Bloomington, Ind.] : Indiana University, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3319892.
Texto completoTitle from PDF t.p. (viewed on May 13, 2009). Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4823. Adviser: Shyh-Yuan Lee.
Gursoy, Ekrem Niebur Dagmar. "Independent component analysis for harmonic source identification in electric power systems /". Philadelphia, Pa. : Drexel University, 2007. http://hdl.handle.net/1860/1781.
Texto completoZhao, Yue. "Independent Component Analysis Enhancements for Source Separation in Immersive Audio Environments". UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/34.
Texto completoLi, Rui Fa. "Advanced process monitoring and control using principal and independent component analysis". Thesis, University of Leeds, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275714.
Texto completoHuang, Huang-Wen y 黃煌文. "Comparison between Network Component Analysis and Independent Component Analysis". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/44160739111819490954.
Texto completo國立中正大學
統計科學所
95
Network component analysis (NCA) and independent component analysis (ICA) both are the ways of redundancy reduction. These statistical methods are for transforming an observed multidimensional random vector into lowerdimension. In this thesis, we use two different algorithms for linear ICA: fast fixed-point algorithm and joint approximate diagonalization of eigenmatrices algorithm. We compare these three techniques that the ability of reconstructing the hidden regulatory layers, via the simulation studies and a real data examples. We also investigate the sensitivity of the reconstructed signals to inaccuracies in the strength of network connectivity.
chang, Lun-ching y 張倫境. "Remarks on Network Component Analysis and Independent Component Analysis". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/02695657099892855016.
Texto completo國立中正大學
統計科學所
96
In this thesis, two algorithms Fast ICA and Jade ICA, in independent component analysis are performed to find the actual sources from the mixing sources. It is well-known that the actual sources must be mutually independent in ICA methods and be far away from the Gaussian distribution. A new method called network component analysis (NCA) can also be applied to blind sources cases without the mutually independence of sources assumption. To illustrate the applications of these approaches, some simulation studies and a real data example are provided in this thesis.
Pan, Jia-Chiun. "Covariate-Adjusted Independent Component Analysis". 2004. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-0207200411343900.
Texto completoLi, Shih-husiung y 李仕雄. "Nonstationary Bayesian Independent Component Analysis". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/15612008396397801403.
Texto completo國立成功大學
資訊工程學系碩博士班
97
In an intelligent speech perception system, it is required to recover speech signals from the mixed signals where some unknown and independent sources are simultaneously acquired by the system microphones. As we known, the independent component analysis (ICA) is a popular approach for blind source separation (BSS) and is referred as an important issue in the fields of machine learning. Traditionally, the standard ICA assumes that the source signals are stationary. This assumption restricts the performance of ICA in real-world applications. Since the source signals may be moving or may be active or inactive as time goes on, we propose a nonstationary Bayesian ICA (NB-ICA) for dealing with the nonstationary blind source separation for an intelligent speech perception system. The proposed NB-ICA algorithm is based on the online Bayesian learning theory which identifies the source activity and estimates the number of source signals in real time. Also, comparing with the other nonstationary ICA algorithms, the computation cost of the proposed NB-ICA can be significantly reduced. In addition, the segmentation information of the source signals is important for the applications of intelligent speech perception such as the systems of speech recognition, speaker identification and speaker diarization. Accordingly, we incorporate a semi-Markov model for capturing the duration information for the estimated status of the source signals. The experimental results show that the proposed NB-ICA algorithm is efficient for the separation of nonstationary source signals in terms of signal-to-inference ratio and detection accuracy of signal segments.
Pan, Jia-Chiun y 潘家群. "Covariate-Adjusted Independent Component Analysis". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/92973510261499725567.
Texto completo國立臺灣大學
流行病學研究所
92
Independent component analysis (ICA) is a recently developed statistical and computational technique for discovering mutually independent nongaussian latent variables from observed multivariate data in the fields of neural networks and signal processing. It can potentially be applied to many application fields such as brain imaging, audio separation, telecommunication, feature extraction, economics, psychology, physiology, biomedical engineering, and bioinformatics, whenever the assumptions of statistical independence and nongaussianity are substantively justifiable. In current practice, one applies the standard procedure(s) of ICA directly to the observed multivariate variables, even though they may be affected by some known covariates, to identify the independent components and estimate the mixing coefficients. In this study, we find that ignoring those relevant covariates may lead to a biased result of ICA, and then suggest a covariate-adjusted ICA to minimize such biases by applying the standard procedure(s) of ICA to the residuals from the regressions of the observed multivariate variables on those relevant covariates in a linear ICA model. A simulation study is conducted using the FastICA algorithm to examine the statistical properties of our covariate-adjusted ICA and to derive numerically the sampling distributions of the estimated mixing coefficients as an interesting by-product. Finally, two examples are given for illustration.
Chia-Ho, Lin y 林家合. "Independent Component Analysis and Its Applications". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/18822129213916587385.
Texto completo國立臺灣大學
電信工程學研究所
91
When we want to find some relationship in large amounts of data, such as data analysis, feature extraction, signal processing, de-noising, and neural network research, a suitable representation of these data or data compression is needed. To achieve these goals, we usually do some linear transformation on original data. For example, principle component analysis, factor analysis, projection pursuit, and blind identification, etc. Recently, a powerful linear transform method called independent component analysis (ICA) is developed. We can say that this is an extension of principle component analysis (PCA), which can only impose uncorrelated up to second order statistics, and thus, defines orthogonal directions. However, ICA does not have these constrains and can analyze higher-order statistics. Independent component analysis can be applied on many applications such as electroencephalogram (EEG), de-noising, remote sensing, watermarking (data encryption or decryption), feature extraction such as facial detection and recognition, texture extraction, and object classification, etc. This thesis consists of five chapters. There will be an introduction to independent component analysis in chapter 1, including problem formulation, related works, some applications and the organization of this thesis. In chapter 2, I will introduce the algorithm of principle component analysis first, and then the theories and algorithms of independent component analysis will be explained. Also I will compare these algorithms and a summary will be made. In chapter 3, I will introduce applications of ICA on image processing, such as watermark, face detection, de-noise, etc. In chapter 4, I will introduce applications of ICA on sound processing, such as separating human speeches or separating human voice from background music .Finally, a concluding remark and future research directions shall be given in chapter 5.
Chen, Ching-Wen y 陳清文. "Independent Component Analysis Applied to Meditation VEP Analysis". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/8k8z2n.
Texto completoMarcynuk, Kathryn L. "Independent component analysis for maternal-fetal electrocardiography". 2015. http://hdl.handle.net/1993/30181.
Texto completo"Extensions of independent component analysis: towards applications". Thesis, 2005. http://library.cuhk.edu.hk/record=b6074028.
Texto completoIn this thesis, first we consider the problem of source separation of post-nonlinear (PNL) mixtures, which is an extension of ICA to the nonlinear mixing case. With a large number of parameters, existing methods are computation-demanding and may be prone to local optima. Based on the fact that linear mixtures of independent variables tend to be Gaussian, we develop a simple and efficient method for this problem, namely extended Gaussianization. With Gaussianization as preprocessing, this method approximates each linear mixture of independent sources by the Cornish-Fisher expansion with only two parameters. Inspired by the relationship between the PNL mixing model and the Wiener system, extended Gaussianization is also proposed for blind inversion of Wiener systems.
Independent component analysis (ICA) is a recent and powerful technique for recovering latent independent sources given only their mixtures. The basic ICA model assumes that sources are linearly mixed and mutually independent.
Next, we study the subband decomposition ICA (SDICA) model, which extends the basic ICA model to allow dependence between sources by assuming that only some narrow-band source sub-components are independent. In SDICA, it is difficult to determine the subbands of source independent sub-components. We discuss the feasibility of performing SDICA in an adaptive manner. An adaptive method, called band selective ICA, is then proposed for this task. We also investigate the relationship between overcomplete ICA and SDICA and show that band selective ICA can solve the overcomplete ICA problems with sources having specific frequency localizations. Experimental results on separating images of human faces as well as artificial data are presented to verify the powerfulness of band selective ICA.
Zhang Kun.
"July 2005."
Adviser: Lai-Wan Chan.
Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3925.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (p. 218-234).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract in English and Chinese.
School code: 1307.
Yang, Yi-Cyun y 楊逸群. "Nonlinear Independent Component Analysis using Generalized Adalines". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/37318403517918691791.
Texto completo國立東華大學
應用數學系
94
A new method is devised for linear and post-nonlinear independent component analysis. Unlike traditional statistics oriented ICA algorithms, which have been developed based on minimization of the Kullback-Leibler(KL) divergence between retrieved components, this method uses the recurrent optimal post-nonlinear kernel method to realize blind separation of linear or post-nonlinear mixtures of independent sources. The post-nonlinear mixing structure of independent sources is realized by multiple generalized adalines(gadalines). Following the leave-one-out learning strategy, hyper-parameters of each gadaline as well as intermediate independent components are optimized under a mean-field-annealing process to satisfy constraints given by multi-channel observations. The new method is shown more feasible for post-nonlinear independent component analysis. It indeed involves with no computations of optimizing statistics, such as the KL divergence or high order moments, which have been known diffcult to be resolved for nonlinear independent component analysis.
Ejaz, Masood. "A framework for implementing Independent Component Analysis algorithms". 2008. http://etd.lib.fsu.edu/theses/available/etd-04182008-123550.
Texto completoAdvisor: Simon y. Foo, Florida State University, FAMU-FSU College of Engineering, Dept. of Electrical & Computer Engineering. Title and description from dissertation home page (viewed July 23, 2008). Document formatted into pages; contains viii,120 pages. Includes bibliographical references.
Kaplan, Sam T. "Principal and independent component analysis for seismic data". Thesis, 2003. http://hdl.handle.net/2429/14100.
Texto completoLee, Shih-Hua y 李世驊. "A new algorithm for convolutive independent component analysis". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/42596436345836454563.
Texto completo國立東華大學
應用數學系
94
This work addresses on blind separation of convolutive mixtures of independent sources. The temporally convolutive structure is assumed to be composed of multiple mixing matrices, each corresponding to a time delay, collectively transforming a segment of consecutive source signals to form multi-channel observations. As τ=1, this problem reduces to linear independent component analysis. For arbitrary τ, we propose a new algorithm to estimate the unknown convolutive structure as well as independent sources. The proposed convolutive ICA algorithm is based on optimal kernel estimation and leave-one-out approximation operated under the mean-field-annealing process. We test the new algorithm with artificially created data and two-microphone recordings of speech and musics. It is shown that the error between the estimated and given convolutive structures is significantly reduced for artificially created data and the human speech is well separated from the background musics for the two-microphone recordings. The proposed new algorithm is empirically shown effective for blind separation of convolutive mixtures of independent sources. Keyword: Convolutive mixture, independent component analysis, blind separation, optimal kernel estimation, leave-one-out approximation, K-state transfer function, mean field annealing.
Chien, Wei-Nan y 簡瑋男. "Chinese Near-Synonym Substitution Using Independent Component Analysis". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/87414927958735528785.
Texto completo元智大學
資訊管理學系
99
Near-synonym sets represent groups of words with similar meaning, which are useful knowledge resources for many natural language applications such as query expansion for information retrieval (IR) and computer-assisted language learning. However, near-synonyms are not necessarily interchangeable in contexts due to their specific usage and syntactic constraints. Previous studies have developed various methods for near-synonym choice in English sentences. To our best knowledge, there is no such evaluation on Chinese sentences. Therefore, this paper proposes the use of the independent component analysis (ICA) for Chinese near-synonym choice evaluation. Experimental results show that the ICA achieves higher accuracy than the pointwise mutual information (PMI), 5-gram language model and vector space model (VSM) that have been used in previous studies.
Chen, Yuh-Sheng y 陳鈺聖. "Density estimation based post-nonlinear independent component analysis". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/24694095639571985139.
Texto completoHarmeling, Stefan [Verfasser]. "Independent component analysis and beyond / von Stefan Harmeling". 2004. http://d-nb.info/973631805/34.
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