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2004
Springer

Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method

9 years 11 months ago
Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method
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.
Antti Honkela, Stefan Harmeling, Leo Lundqvist, Ha
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ICA
Authors Antti Honkela, Stefan Harmeling, Leo Lundqvist, Harri Valpola
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