A Mutual Information Minimization Approach for a Class of Nonlinear Recurrent Separating Systems

In this work, we deal with nonlinear blind source separation. Our contribution is the derivation of a learning strategy that minimizes the mutual information between the outputs of a class of nonlinear recurrent separating systems. By using the concept of the differential of the mutual information, we obtain an algorithm that does not need a precise knowledge of the source distributions, in contrast to the one obtained by a direct derivation of the minimum mutual information framework, or equally the maximum likelihood approach, for the considered model. The validity of our approach is supported by simulations.