A mutual information minimization approach for a class of nonlinear recurrent separating systems

Leonardo Tomazeli Duarte 1 Christian Jutten 1
1 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
Abstract : 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.
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Submitted on : Tuesday, September 4, 2007 - 11:45:28 AM
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Leonardo Tomazeli Duarte, Christian Jutten. A mutual information minimization approach for a class of nonlinear recurrent separating systems. IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2007), Aug 2007, Thessaloniki, Greece. ⟨hal-00169526⟩

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