Communication Dans Un Congrès Année : 2007

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

Résumé

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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Dates et versions

hal-00169526 , version 1 (04-09-2007)

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  • HAL Id : hal-00169526 , version 1

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Leonardo Tomazeli Duarte, Christian Jutten. A mutual information minimization approach for a class of nonlinear recurrent separating systems. MLSP 2007 - IEEE 17th International Workshop on Machine Learning for Signal Processing, Aug 2007, Thessaloniki, Greece. ⟨hal-00169526⟩
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