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

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.KeywordsCost FunctionIndependent Component AnalysisEnsemble LearningVariational BayesianBlind Signal SeparationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.