PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method
Antti Honkela, Stefan Harmeling, Leo Lundqvist and Harri Valpola
In: 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2004), 22-24 Sep 2004, Granada, Spain.

Abstract

The variational Bayesian nonlinear blind source separation method introduced by Lappalainen and Honkela in 2000 is initialised with linear principal component analysis (PCA). Because of the multilayer perceptron (MLP) network used to model the nonlinearity, the method is susceptible to local minima and therefore sensitive to the initialisation used. As the method is used for nonlinear separation, the linear initialisation may in some cases lead it astray. In this paper we study the use of kernel PCA (KPCA) in the initialisation. KPCA is a rather straightforward generalisation of linear PCA and it is much faster to compute than the variational Bayesian method. The experiments show that it can produce significantly better initialisations than linear PCA. Additionally, the model comparison methods provided by the variational Bayesian framework can be easily applied to compare different kernels.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:105
Deposited By:Antti Honkela
Deposited On:21 May 2004