Post-nonlinear Independent Component Analysis by Variational Bayesian Learning
Identifieur interne : 000190 ( Main/Exploration ); précédent : 000189; suivant : 000191Post-nonlinear Independent Component Analysis by Variational Bayesian Learning
Auteurs : Alexander Ilin [Finlande] ; Antti Honkela [Finlande]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
Abstract
Abstract: Post-nonlinear (PNL) independent component analysis(ICA) is a generalisation of ICA where the observations are assumed to have been generated from independent sources by linear mixing followed by component-wise scalar nonlinearities. Most previous PNL ICA algorithms require the post-nonlinearities to be invertible functions. In this paper, we present a variational Bayesian approach to PNL ICA that also works for non-invertible post-nonlinearities. The method is based on a generative model with multi-layer perceptron (MLP) networks to model the post-nonlinearities. Preliminary results with a difficult artificial example are encouraging.
Url:
DOI: 10.1007/978-3-540-30110-3_97
Affiliations:
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