PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Blind Separation of Nonlinear Mixtures by Variational Bayesian Learning
Antti Honkela, Harri Valpola, Alexander Ilin and Juha Karhunen
Digital Signal Processing Volume 17, Number 5, pp. 914-934, 2007. ISSN 1051-2004

Abstract

Blind separation of sources from nonlinear mixtures is a challenging and often ill-posed problem. We present three methods for solving this problem: an improved nonlinear factor analysis (NFA) method using a multilayer perceptron (MLP) network to model the nonlinearity, a hierarchical NFA (HNFA) method suitable for larger problems and a post-nonlinear NFA (PNFA) method for more restricted post-nonlinear mixtures. The methods are based on variational Bayesian learning, which provides the needed regularisation and allows for easy handling of missing data. While the basic methods are incapable of recovering the correct rotation of the source space, they can discover the underlying nonlinear manifold and allow reconstruction of the original sources using standard linear independent component analysis (ICA) techniques.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3236
Deposited By:Antti Honkela
Deposited On:27 January 2008