Variational Bayesian Independent Component Analysis

Neil D. Lawrence; Christopher M. Bishop

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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.

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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 = 	 {/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 /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  - /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 /publications/lawrence-ica99.html.