Class of Neural Networks for Independent Component Analysis

摘要

Independent Component Analysis (ICA) is a recently developed, useful extension ofstandard Principal Component Analysis (PCA). The ICA model is utilized mainly in blind separation of unknown source signals from their linear mixtures, where only the coefficients of the ICA expansion are of interest. In this paper, we propose neural structures related to multilayer feed-forward networks for performing complete ICA. The basic ICA network consists of whitening, separation, and basis vector estimation layers. It can be used for both blind source separation and estimation of the basis vectors of ICA. We consider learning algorithms for each layer, and modify our previous nonlinear PCA algorithms so that their separation capabilities are greatly improved. The proposed class of networks yields good results in test examples using both artificial and real-world data. (Copyright (c) 1995 Helsinki University of Technology.)