Variational Bayesian Independent Component Analysis

Neil D. Lawrence, Christopher M. Bishop
, 2000.

Abstract

Blind separation of signals through the info-max algorithm may be viewed as maximum likelihood learning in a latent variable model. In this paper we present an alternative approach to maximum likelihood learning in these models, namely Bayesian inference. It has already been shown how Bayesian inference can be applied to determine latent dimensionality in principal component analysis models @Bishop:bayesPCA98. In this paper we derive a similar approach for removing unecessary source dimensions in an independent component analysis model. We present results on a toy data-set and on some artificially mixed images.

Cite this Paper

BibTeX
@Misc{Lawrence:ICA99, title = {Variational {B}ayesian Independent Component Analysis}, author = {Lawrence, Neil D. and Bishop, Christopher M.}, year = {2000}, pdf = {http://www.thelawrences.net/neil/bica_report.pdf}, url = {http://inverseprobability.com/publications/lawrence-ica99.html}, abstract = {Blind separation of signals through the info-max algorithm may be viewed as maximum likelihood learning in a latent variable model. In this paper we present an alternative approach to maximum likelihood learning in these models, namely Bayesian inference. It has already been shown how Bayesian inference can be applied to determine latent dimensionality in principal component analysis models @Bishop:bayesPCA98. In this paper we derive a similar approach for removing unecessary source dimensions in an independent component analysis model. We present results on a toy data-set and on some artificially mixed images.} }
Endnote
%0 Generic %T Variational Bayesian Independent Component Analysis %A Neil D. Lawrence %A Christopher M. Bishop %D 2000 %F Lawrence:ICA99 %U http://inverseprobability.com/publications/lawrence-ica99.html %X Blind separation of signals through the info-max algorithm may be viewed as maximum likelihood learning in a latent variable model. In this paper we present an alternative approach to maximum likelihood learning in these models, namely Bayesian inference. It has already been shown how Bayesian inference can be applied to determine latent dimensionality in principal component analysis models @Bishop:bayesPCA98. In this paper we derive a similar approach for removing unecessary source dimensions in an independent component analysis model. We present results on a toy data-set and on some artificially mixed images.
RIS
TY - GEN TI - Variational Bayesian Independent Component Analysis AU - Neil D. Lawrence AU - Christopher M. Bishop DA - 2000/01/01 ID - Lawrence:ICA99 L1 - http://www.thelawrences.net/neil/bica_report.pdf UR - http://inverseprobability.com/publications/lawrence-ica99.html AB - Blind separation of signals through the info-max algorithm may be viewed as maximum likelihood learning in a latent variable model. In this paper we present an alternative approach to maximum likelihood learning in these models, namely Bayesian inference. It has already been shown how Bayesian inference can be applied to determine latent dimensionality in principal component analysis models @Bishop:bayesPCA98. In this paper we derive a similar approach for removing unecessary source dimensions in an independent component analysis model. We present results on a toy data-set and on some artificially mixed images. ER -
APA
Lawrence, N.D. & Bishop, C.M.. (2000). Variational Bayesian Independent Component Analysis. Available from http://inverseprobability.com/publications/lawrence-ica99.html.

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