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Erschienen in:
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2012 | OriginalPaper | Buchkapitel

Independent Component Analysis

verfasst von : Seungjin Choi

Erschienen in: Handbook of Natural Computing

Verlag: Springer Berlin Heidelberg

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Abstract

Independent component analysis (ICA) is a statistical method, the goal of which is to decompose multivariate data into a linear sum of non-orthogonal basis vectors with coefficients (encoding variables, latent variables, and hidden variables) being statistically independent. ICA generalizes widely used subspace analysis methods such as principal component analysis (PCA) and factor analysis, allowing latent variables to be non-Gaussian and basis vectors to be non-orthogonal in general. ICA is a density-estimation method where a linear model is learned such that the probability distribution of the observed data is best captured, while factor analysis aims at best modeling the covariance structure of the observed data. We begin with a fundamental theory and present various principles and algorithms for ICA.

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Vorheriges Kapitel Nonlinear Process Modelling and Control Using Neurofuzzy Networks
Nächstes Kapitel Neural Networks for Time-Series Forecasting
Literatur
Amari S, Cardoso JF (1997) Blind source separation: semiparametric statistical approach. IEEE Trans Signal Process 45:2692–2700CrossRef
Amari S, Chen TP, Cichocki A (1997) Stability analysis of learning algorithms for blind source separation. Neural Networks 10(8):1345–1351CrossRef
Amari S, Cichocki A (1998) Adaptive blind signal processing – neural network approaches. Proc IEEE (Special Issue on Blind Identification and Estimation) 86(10):2026–2048
Amari S, Cichocki A, Yang HH (1996) A new learning algorithm for blind signal separation. In: Touretzky DS, Mozer MC, Hasselmo ME (eds) Advances in neural information processing systems (NIPS), vol 8. MIT Press, Cambridge, pp 757–763
Attias H (1999) Independent factor analysis. Neural Comput 11:803–851CrossRef
Bach F, Jordan MI (2002) Kernel independent component analysis. JMLR 3:1–48MathSciNet
Bach FR, Jordan MI (2003) Beyond independent components: trees and clusters. JMLR 4:1205–1233MathSciNet
Bell A, Sejnowski T (1995) An information maximisation approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159CrossRef
Belouchrani A, Abed‐Merain K, Cardoso JF, Moulines E (1997) A blind source separation technique using second order statistics. IEEE Trans Signal Process 45:434–444CrossRef
Cardoso JF (1989) Source separation using higher‐order moments. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP), Paris, France, 23–26 May 1989
Cardoso JF (1997) Infomax and maximum likelihood for source separation. IEEE Signal Process Lett 4(4):112–114CrossRef
Cardoso JF, Laheld BH (1996) Equivariant adaptive source separation. IEEE Trans Signal Process 44(12):3017–3030CrossRef
Cardoso JF, Souloumiac A (1993) Blind beamforming for non Gaussian signals. IEE Proc‐F 140(6):362–370
Chang C, Ding Z, Yau SF, Chan FHY (2000) A matrix‐pencil approach to blind separation of colored nonstationary signals. IEEE Trans Signal Process 48(3):900–907CrossRef
Choi S (2002) Adaptive differential decorrelation: a natural gradient algorithm. In: Proceedings of the international conference on artificial neural networks (ICANN), Madrid, Spain. Lecture notes in computer science, vol 2415. Springer, Berlin, pp 1168–1173
Choi S (2003) Differential learning and random walk model. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP), IEEE, Hong Kong, pp 724–727
Choi S, Cichocki A (2000a) Blind separation of nonstationary and temporally correlated sources from noisy mixtures. In: Proceedings of IEEE workshop on neural networks for signal processing, IEEE, Sydney, Australia. pp 405–414
Choi S, Cichocki A (2000b) Blind separation of nonstationary sources in noisy mixtures. Electron Lett 36(9):848–849CrossRef
Choi S, Cichocki A, Amari S (2000) Flexible independent component analysis. J VLSI Signal Process 26(1/2):25–38MATHCrossRef
Choi S, Cichocki A, Belouchrani A (2002) Second order nonstationary source separation. J VLSI Signal Process 32:93–104MATHCrossRef
Choi S, Cichocki A, Park HM, Lee SY (2005) Blind source separation and independent component analysis: a review. Neural Inf Process Lett Rev 6(1): 1–57
Cichocki A, Amari S (2002) Adaptive blind signal and image processing: learning algorithms and applications. Wiley, ChichesterCrossRef
Cichocki A, Unbehauen R (1996) Robust neural networks with on‐line learning for blind identification and blind separation of sources. IEEE Trans Circ Syst Fund Theor Appl 43:894–906CrossRef
Comon P (1994) Independent component analysis, a new concept? Signal Process 36(3):287–314MATHCrossRef
Fukunaga K (1990) An introduction to statistical pattern recognition. Academic, New York
Golub GH, Loan CFV (1993) Matrix computations, 2nd edn. Johns Hopkins, Baltimore
Haykin S (2000) Unsupervised adaptive filtering: blind source separation. Prentice‐Hall
Hyvärinen A (1999) Survey on independent component analysis. Neural Comput Surv 2:94–128
Hyvärinen A, Hoyer P (2000) Emergence of phase‐ and shift‐invariant features by decomposition of natural images into independent feature subspaces. Neural Comput 12(7):1705–1720CrossRef
Hyvärinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New YorkCrossRef
Hyvärinen A, Oja E (1997) A fast fixed‐point algorithm for independent component analysis. Neural Comput 9:1483–1492CrossRef
Jutten C, Herault J (1991) Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture. Signal Process 24:1–10MATHCrossRef
Karhunen J (1996) Neural approaches to independent component analysis and source separation. In: Proceedings of the European symposium on artificial neural networks (ESANN), Bruges, Belgium, pp 249–266
Kim S, Choi S (2005) Independent arrays or independent time courses for gene expression data. In: Proceedings of the IEEE international symposium on circuits and systems (ISCAS), Kobe, Japan, 23–26 May 2005
Kosko B (1986) Differential Hebbian learning. In: Proceedings of American Institute of Physics: neural networks for computing, Snowbird. American Institute of Physics, Woodbury, pp 277–282
Lee TW (1998) Independent component analysis: theory and applications. KluwerMATH
Lee TW, Girolami M, Sejnowski T (1999) Independent component analysis using an extended infomax algorithm for mixed sub‐Gaussian and super‐Gaussian sources. Neural Comput 11(2):609–633CrossRef
Lewicki MS, Sejnowski T (2000) Learning overcomplete representation. Neural Comput 12(2):337–365CrossRef
Li Y, Cichocki A, Amari S (2006) Blind estimation of channel parameters and source components for EEG signals: a sparse factorization approach. IEEE Trans Neural Networ 17(2):419–431CrossRef
Liebermeister W (2002) Linear modes of gene expression determined by independent component analysis. Bioinformatics 18(1):51–60CrossRef
MacKay DJC (1996) Maximum likelihood and covariant algorithms for independent component analysis. Technical Report Draft 3.7, University of Cambridge, Cavendish Laboratory
Matsuoka K, Ohya M, Kawamoto M (1995) A neural net for blind separation of nonstationary signals. Neural Networks 8(3):411–419CrossRef
Miskin JW, MacKay DJC (2001) Ensemble learning for blind source separation. In: Roberts S, Everson R (eds) Independent component analysis: principles and practice. Cambridge University Press, Cambridge, UK, pp 209–233
Molgedey L, Schuster HG (1994) Separation of a mixture of independent signals using time delayed correlations. Phys Rev Lett 72:3634–3637CrossRef
Oja E (1995) The nonlinear PCA learning rule and signal separation – mathematical analysis. Technical Report A26, Helsinki University of Technology, Laboratory of Computer and Information Science
Pearlmutter B, Parra L (1997) Maximum likelihood blind source separation: a context‐sensitive generalization of ICA. In: Mozer MC, Jordan MI, Petsche T (eds) Advances in neural information processing systems (NIPS), vol 9. MIT Press, Cambridge, pp 613–619
Pham DT (1996) Blind separation of instantaneous mixtures of sources via an independent component analysis. IEEE Trans Signal Process 44(11): 2768–2779CrossRef
Plumbley MD (2003) Algorithms for nonnegative independent component analysis. IEEE Trans Neural Network 14(3):534–543CrossRef
Stone JV (2004) Independent component analysis: a tutorial introduction. MIT Press, Cambridge
Stone JV, Porrill J, Porter NR, Wilkinson IW (2002) Spatiotemporal independent component analysis of event‐related fMRI data using skewed probability density functions. NeuroImage 15(2):407–421CrossRef
Tong L, Soon VC, Huang YF, Liu R (1990) AMUSE: a new blind identification algorithm. In: Proceedings of the IEEE international symposium on circuits and systems (ISCAS), IEEE, New Orleans, pp 1784–1787
Welling M, Weber M (2001) A constrained EM algorithm for independent component analysis. Neural Comput 13:677–689MATHCrossRef
Metadaten
Titel
Independent Component Analysis
verfasst von
Seungjin Choi
Copyright-Jahr
2012
Verlag
Springer Berlin Heidelberg
Buch
Handbook of Natural Computing

Print ISBN: 978-3-540-92909-3

Electronic ISBN: 978-3-540-92910-9

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-3-540-92910-9_13

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