Abstract

: Independent Component Analysis (ICA) is a recently developed, useful extension of standard Principal Component Analysis (PCA). The associated linear model is used mainly in source separation, where only the coefficients of the ICA expansion are of interest. In this paper, we propose a neural structure related to nonlinear PCA networks for estimating the basis vectors of ICA. This ICA network consists of whitening, separation, and estimation layers, and yields good results in test examples. We also modify our previous nonlinear PCA algorithms so that their separation capabilities are greatly improved. 1. Introduction Currently, there is a growing interest among neural network researchers in unsupervised learning beyond PCA, often called nonlinear PCA. Such methods take into account higher-order statistics, and are often more competitive than standard PCA when realized neurally [9, 10]. Nonlinear PCA type methods can be developed from various starting points, usually leading to mutua...

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