36Neural Estimation of Basis Vectors in Independent Component Analysis

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 coeecients 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.