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Dissertations / Theses on the topic 'Independent component analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Gao, Pei. "Nonlinear independent component analysis." Thesis, University of Newcastle Upon Tyne, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437979.

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2

Harmeling, Stefan. "Independent component analysis and beyond." Phd thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973631805.

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3

Blaschke, 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.

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Der Fokus dieser Dissertation liegt auf den Verbindungen zwischen ICA (Independent Component Analysis - Unabhängige Komponenten Analyse) und SFA (Slow Feature Analysis - Langsame Eigenschaften Analyse). Um einen Vergleich zwischen beiden Methoden zu ermöglichen wird CuBICA2, ein ICA Algorithmus basierend nur auf Statistik zweiter Ordnung, d.h. Kreuzkorrelationen, vorgestellt. Dieses Verfahren minimiert zeitverzögerte Korrelationen zwischen Signalkomponenten, um die statistische Abhängigkeit zwischen denselben zu reduzieren. Zusätzlich wird eine alternative SFA-Formulierung vor

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4

Brock, James L. "Acoustic classification using independent component analysis /." Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/2067.

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5

Papathanassiou, Christos. "Independent component analysis of magnetoencephalographic signals." Thesis, University of Surrey, 2003. http://epubs.surrey.ac.uk/771941/.

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Magnetoencephalography (MEG) is a non-invasive brain imaging technique which allows instant tracking of changes in brain activity. However, it is affected by strong artefact signals generated by the heart or the eye blinking. The blind source separation problem is typically encountered in MEG studies when a set of unknown signals, originating from different sources inside or outside the brain, is mixed with an also unknown mixing matrix during their recording. Independent component analysis (ICA) is a recently developed technique which aims to estimate the original sources given only the obser

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6

Miskin, James William. "Ensemble learning for independent component analysis." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621116.

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7

Garvey, 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.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2007.<br>Thesis Advisor(s): Frank E. Kragh. "June 2007." Includes bibliographical references (p. 103). Also available in print.

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8

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.

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9

Choudrey, 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.

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10

Kalkan, 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.

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11

Björling, 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.

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<p>Denna 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 typisk

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12

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.

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13

Wu, 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.

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14

Beckmann, 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.

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15

吳浩存 and 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.

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16

Fiore, Ugo. "Improving Network Anomaly Detection with Independent Component Analysis." Doctoral thesis, Universita degli studi di Salerno, 2015. http://hdl.handle.net/10556/1978.

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2013 - 2014<br>Complexity, sophistication, and rate of growth of modern networks, coupled with the depth, continuity, and pervasiveness of their role in our everyday lives, stress the importance of identifying potential misuse or threats that could undermine regular operation. To ensure an adequate and prompt reaction, anomalies in network traffic should be detected, classified, and identified as quickly and correctly as possible. Several approaches focus on inspecting the content of packets traveling through the network, while other techniques aim at detecting suspicious activity by measuri

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17

Robila, 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.

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18

Baylor, 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.

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19

E, Okwelume Gozie, and 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.

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Our thesis work focuses on Frequency-domain Blind Source Separation (BSS) in which the received mixed signals are converted into the frequency domain and Independent Component Analysis (ICA) is applied to instantaneous mixtures at each frequency bin. Computational complexity is also reduced by using this method. We also investigate the famous problem associated with Frequency-Domain Blind Source Separation using ICA referred to as the Permutation and Scaling ambiguities, using methods proposed by some researchers. This is our main target in this project; to solve the permutation and scaling am

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20

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.

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21

Rezaee, 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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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.<br>Submitted by Fernanda Percia França (fernandafranca@bce.unb.br) on 2016-02-22T20:16:00Z No. of bitstreams: 1 2015_SayedMajidRezaee.pdf: 1299294 bytes, checksum: 5ae8992f8de2423bc23cf06bdeaeeb09 (MD5)<br>Approved for entry into archive by Marília Freitas(marilia@bce.unb.br) on 2016-05-26T16:32:15Z (GMT) No. of bitstreams: 1 2015_SayedMajidRezaee.pdf: 1299294 bytes, checksum: 5ae8992f8de2423bc23cf06bdeaeeb09 (MD5)<br>Made available in DSpace on 2016-05-26T16:32:15Z (GMT). N

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22

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.

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The short form SF-36 survey is a widely used survey of patient health related quality of life. It yields eight subscale scores of functional health and well-being that are summarized by two physical and mental component summary scores. However, recent studies have reported inconsistent results between the eight subscales and the two component summary measures when the scores are from a sick population. They claim that this problem is due to the method used to compute the SF-36 component summary scores, which is based on principal component analysis with orthogonal rotation.In this thesis, we e

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23

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.

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24

Afsari, Bijan. "Gradient flow based matrix joint diagonalization for independent component analysis." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1352.

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Thesis (M.S.) -- University of Maryland, College Park, 2004.<br>Thesis 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.

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25

Elsabrouty, Maha. "Riemannian geometry based blind signal separation using independent component analysis." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/29291.

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Blind Source Separation is one of the newest and most active research areas in adaptive filtering. It represents the solution for many real situations in the audio, speech processing and telecommunication fields. The word "blind" reflects the fact that neither the source nor the mixing channel is known. This is, clearly, a more difficult situation compared to conventional adaptive filtering problems. Algorithms developed for blind separation reflect this difficulty. They possess a higher degree of sophistication compared with algorithms in other adaptive filtering approaches. The cost function

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26

Sarfraz, M. "Role of independent component analysis in intelligent ECG signal processing." Thesis, University of Salford, 2014. http://usir.salford.ac.uk/33200/.

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The Electrocardiogram (ECG) reflects the activities and the attributes of the human heart and reveals very important hidden information in its structure. The information is extracted by means of ECG signal analysis to gain insights that are very crucial in explaining and identifying various pathological conditions. The feature extraction process can be accomplished directly by an expert through, visual inspection of ECGs printed on paper or displayed on a screen. However, the complexity and the time taken for the ECG signals to be visually inspected and manually analysed means that it‟s a very

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27

Zakeri, Zohreh. "Optimised use of independent component analysis for EEG signal processing." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7430/.

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Electroencephalography (EEG) is the prevalent technique for monitoring brain function. It employs a set of electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of unwanted signals, which are known as artefacts, usually mix with the EEG at any point during the recording process. As the amplitudes of the EEG and ERPs are very small (in the order of microvolts), they can be buried in the artefacts which have very high amplitudes in the

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28

Wang, Suogang. "Enhancing brain-computer interfacing through advanced independent component analysis techniques." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/65897/.

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A brain-computer interface (BCI) is a direct communication system between a brain and an external device in which messages or commands sent by an individual do not pass through the brain’s normal output pathways but is detected through brain signals. Some severe motor impairments, such as Amyothrophic Lateral Sclerosis, head trauma, spinal injuries and other diseases may cause the patients to lose their muscle control and become unable to communicate with the outside environment. Currently no effective cure or treatment has yet been found for these diseases. Therefore using a BCI system to reb

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29

Vigon, Laurence Celine. "Independent component analysis techniques and their performance evaluation for electroencephalography." Thesis, Sheffield Hallam University, 2002. http://shura.shu.ac.uk/20479/.

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The ongoing electrical activity of the brain is known as the electroencephalogram (EEG). Evoked potentials (EPs) are voltage deviations in the EEG elicited in association with stimuli. EPs provide clinical information by allowing an insight into neurological processes. The amplitude of EPs is typically several times less than the background EEG. The background EEG has the effect of obscuring the EPs and therefore appropriate signal processing is required for their recovery. The EEG waveforms recorded from electrodes placed on the scalp contains the ongoing background EEG, EPs from various brai

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30

Kent, 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.

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We find that ICA could make a useful contribution through the identification of the land based seasonal cycle and the ocean based seasonal cycle. These qualities mean that ICA may further prove a useful tool in the problem of identifying components of climate change signals from ensembles of multiple climate models.

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31

Johnson, 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.

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32

Wang, 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.

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Thesis (Ph.D.)--Indiana University, Dept. of Physics, 2008.<br>Title from PDF t.p. (viewed on May 13, 2009). Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4823. Adviser: Shyh-Yuan Lee.

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33

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.

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34

Zhao, Yue. "Independent Component Analysis Enhancements for Source Separation in Immersive Audio Environments." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/34.

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In immersive audio environments with distributed microphones, Independent Component Analysis (ICA) can be applied to uncover signals from a mixture of other signals and noise, such as in a cocktail party recording. ICA algorithms have been developed for instantaneous source mixtures and convolutional source mixtures. While ICA for instantaneous mixtures works when no delays exist between the signals in each mixture, distributed microphone recordings typically result various delays of the signals over the recorded channels. The convolutive ICA algorithm should account for delays; however, it re

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35

Li, 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.

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36

Prasad, P. Shiva. "Independent Component Analysis." Thesis, 2007. http://ethesis.nitrkl.ac.in/52/2/10307008.pdf.

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A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method

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37

Huang, Huang-Wen, and 黃煌文. "Comparison between Network Component Analysis and Independent Component Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/44160739111819490954.

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碩士<br>國立中正大學<br>統計科學所<br>95<br>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 sensitiv

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chang, Lun-ching, and 張倫境. "Remarks on Network Component Analysis and Independent Component Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/02695657099892855016.

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碩士<br>國立中正大學<br>統計科學所<br>96<br>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.

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39

Pan, Jia-Chiun. "Covariate-Adjusted Independent Component Analysis." 2004. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-0207200411343900.

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40

Li, Shih-husiung, and 李仕雄. "Nonstationary Bayesian Independent Component Analysis." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/15612008396397801403.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>97<br>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 s

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Pan, Jia-Chiun, and 潘家群. "Covariate-Adjusted Independent Component Analysis." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/92973510261499725567.

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碩士<br>國立臺灣大學<br>流行病學研究所<br>92<br>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 justi

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42

Chia-Ho, Lin, and 林家合. "Independent Component Analysis and Its Applications." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/18822129213916587385.

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碩士<br>國立臺灣大學<br>電信工程學研究所<br>91<br>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 ext

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Chen, Ching-Wen, and 陳清文. "Independent Component Analysis Applied to Meditation VEP Analysis." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/8k8z2n.

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Marcynuk, Kathryn L. "Independent component analysis for maternal-fetal electrocardiography." 2015. http://hdl.handle.net/1993/30181.

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Separating unknown signal mixtures into their constituent parts is a difficult problem in signal processing called blind source separation. One of the benchmark problems in this area is the extraction of the fetal heartbeat from an electrocardiogram in which it is overshadowed by a strong maternal heartbeat. This thesis presents a study of a signal separation technique called independent component analysis (ICA), in order to assess its suitability for the maternal-fetal ECG separation problem. This includes an analysis of ICA on deterministic, stochastic, simulated and recorded ECG signals. Th

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"Extensions of independent component analysis: towards applications." Thesis, 2005. http://library.cuhk.edu.hk/record=b6074028.

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In practice, the application and extension of the ICA model depend on the problem and the data to be investigated. We finally focus on GARCH models in finance, and show that estimation of univariate or multivariate GARCH models is actually a nonlinear ICA problem; maximizing the likelihood is equivalent to minimizing the statistical dependence in standardized residuals. ICA can then be used for factor extraction in multivariate factor GARCH models. We also develop some extensions of ICA for this task. These techniques for extracting factors from multivariate return series are compared both the

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Yang, Yi-Cyun, and 楊逸群. "Nonlinear Independent Component Analysis using Generalized Adalines." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/37318403517918691791.

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碩士<br>國立東華大學<br>應用數學系<br>94<br>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 s

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47

Ejaz, Masood. "A framework for implementing Independent Component Analysis algorithms." 2008. http://etd.lib.fsu.edu/theses/available/etd-04182008-123550.

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Thesis (Ph. D.)--Florida State University, 2008.<br>Advisor: 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.

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48

Kaplan, Sam T. "Principal and independent component analysis for seismic data." Thesis, 2003. http://hdl.handle.net/2429/14100.

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Principal and Independent component analysis (PCA and ICA) are two ideas which are very much related; both employing a statistical understanding of data to achieve their goals. Whereas PCA exploits statistical correlation, ICA uses statistical independence to glean useful information from data. Seismic data is inherently noisy, and is complicated by the presence of an unknown seismic wavelet. Analysis of the data is aided by, both, noise suppression and blind deconvolution techniques. First, consider the subject of noise suppression. If the data are organized into several sequences where, from

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49

Lee, Shih-Hua, and 李世驊. "A new algorithm for convolutive independent component analysis." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/42596436345836454563.

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碩士<br>國立東華大學<br>應用數學系<br>94<br>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

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50

Chien, Wei-Nan, and 簡瑋男. "Chinese Near-Synonym Substitution Using Independent Component Analysis." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/87414927958735528785.

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碩士<br>元智大學<br>資訊管理學系<br>99<br>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

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