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MISEP - Linear and Nonlinear ICA Based on Mutual Information

Almeida, Luis B. (2003) MISEP - Linear and Nonlinear ICA Based on Mutual Information. [Journal (On-line/Unpaginated)] (Unpublished)

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Abstract

MISEP is a method for linear and nonlinear ICA, that is able to handle a large variety of situations. It is an extension of the well known INFOMAX method, in two directions: (1) handling of nonlinear mixtures, and (2) learning the nonlinearities to be used at the outputs. The method can therefore separate linear and nonlinear mixtures of components with a wide range of statistical distributions. This paper presents the basis of the MISEP method, as well as experimental results obtained with it. The results illustrate the applicability of the method to various situations, and show that, although the nonlinear blind separation problem is ill-posed, use of regularization allows the problem to be solved when the nonlinear mixture is relatively smooth.

Item Type:Journal (On-line/Unpaginated)
Additional Information:This is a submitted paper, which is undergoing review. If accepted, the final version will be posted here.
Keywords:Independent component analysis, blind source separation, nonlinear, ICA, BSS, MISEP
Subjects:Computer Science > Statistical Models
Computer Science > Machine Learning
Computer Science > Artificial Intelligence
ID Code:2856
Deposited By:Almeida, Prof. Luis B.
Deposited On:30 Mar 2003
Last Modified:12 Sep 2007 17:47

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

A. Hyvarinen, J. Karhunen, E. Oja, Independent component analysis, J. Wiley, 2001.

A. Taleb, C. Jutten, Batch algorithm for source separation on postnonlinar mixtures, in: J. F. Cardoso, C. Jutten, P. Loubaton (Eds.), Proc. First Int. Worksh. Independent Component Analysis and Signal Separation, Aussois, France, 1999, pp. 155–160.

J. Schmidhuber, Learning factorial codes by predictability minimization, Neural Computation 4 (6) (1992) 863–879.

G. Burel, Blind separation of sources: A nonlinear neural algorithm, Neural Networks 5 (6) (1992) 937–947.

G. Deco, W. Brauer, Nonlinear higher-order statistical decorrelation by volumeconserving neural architectures, Neural Networks 8 (1995) 525–535.

G. C. Marques, L. B. Almeida, An objective function for independence, in: Proc. International Conference on Neural Networks, Washington DC, 1996, pp. 453–457.

T.-W. Lee, Nonlinear approaches to independent component analysis, Proceedings of the American Institute of Physics October 1999.

F. Palmieri, D. Mattera, A. Budillon, Multi-layer independent component analysis (MLICA), in: J. F. Cardoso, C. Jutten, P. Loubaton (Eds.), Proc. First Int. Worksh. Independent Component Analysis and Signal Separation, Aussois, France, 1999, pp. 93–97.

G. C. Marques, L. B. Almeida, Separation of nonlinear mixtures using pattern repulsion, in: J. F. Cardoso, C. Jutten, P. Loubaton (Eds.), Proc. First Int. Worksh. Independent Component Analysis and Signal Separation, Aussois, France, 1999, pp. 277–282.

H. Valpola, Nonlinear independent component analysis using ensemble learning: Theory, in: Proc. Second Int. Worksh. Independent Component Analysis and Blind Signal Separation, Helsinki, Finland, 2000, pp. 251–256.

L. B. Almeida, Linear and nonlinear ICA based on mutual information, in: Proc. Symp. 2000 on Adapt. Sys. for Sig. Proc., Commun. and Control, Lake Louise, Alberta, Canada, 2000.

S. Harmeling, et al., Nonlinear blind source separation using kernel feature spaces, in: T.-W. Lee (Ed.), Proc. Int. Worksh. Independent Component Analysis and Blind Signal Separation, 2001.

A. Bell, T. Sejnowski, An information-maximization approach to blind separation and blind deconvolution, Neural Computation 7 (1995) 1129–1159.

P. Comon, Independent component analysis – a new concept?, Signal Processing 36 (1994) 287–314.

J.-F. Cardoso, Infomax and maximum likelihood for source separation, IEEE Letters on Signal Processing 4 (1997) 112–114.

T.-W. Lee, M. Girolami, T. Sejnowski, Independent component analysis using an extended infomax algorithm for mixed sub-gaussian and super-gaussian sources, Neural Computation 11 (1999) 417–441.

L. B. Almeida, Multilayer perceptrons, in: E. Fiesler, R. Beale (Eds.), Handbook of Neural Computation, Institute of Physics, Oxford University Press, 1997, available at http://www.iop.org/Books/CIL/HNC/pdf/NCC1_2.PDF.

L. B. Almeida, ICA of linear and nonlinear mixtures based on mutual information, in: Proc. 2001 Int. Joint Conf. on Neural Networks, Washington, D.C., 2001.

L. B. Almeida, Simultaneous MI-based estimation of independent components and of their distributions, in: Proc. Second Int. Worksh. Independent Component Analysis and Blind Signal Separation, Helsinki, Finland, 2000, pp. 169–174.

L. B. Almeida, Faster training in nonlinear ICA using MISEP, in: Proc. Fourth Int. Symp. on Independent Component Analysis and Blind Signal Separation, Nara, Japan, 2003.

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