Information-theoretic approach to blind separation of sources in non-linear mixture

The linear mixture model is assumed in most of the papers devoted to blind separation. A more realistic model for mixture should be non-linear. In this paper, a two-layer perceptron is used as a de-mixing system to separate sources in non-linear mixture, The learning algorithms for the de-mixing system are derived by two approaches: maximum entropy and minimum mutual information. The algorithms derived from the two approaches have a common structure, The new learning equations for the hidden layer are different from the learning equations for the output layer. The natural gradient descent method is applied in maximizing entropy and minimizing mutual information. The information (entropy or mutual information) back-propagation method is proposed to derive the learning equations for the hidden layer. (C) 1998 Elsevier Science B.V. All rights reserved.