Nonlinear blind source separation using kernels

Dominique Martinez 1 Alistair Bray 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We derive a new method for solving nonlinear blind source separation problems by exploiting second-order statistics in a kernel induced feature space. This paper extends a new and efficient closed-form linear algorithm to the non-linear domain using `the kernel trick' originally applied in Support Vector Machines. This technique could likewise be applied to other linear covariance-based source separation algorithms. Experiments on realistic nonlinear mixtures of speech signals, gas multisensor data and visual disparity data illustrate the applicability of our approach.
Type de document :
Article dans une revue
IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers, 2003, 14 (1), pp.228-236
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https://hal.inria.fr/inria-00099523
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Soumis le : mardi 26 septembre 2006 - 09:38:20
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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  • HAL Id : inria-00099523, version 1

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Dominique Martinez, Alistair Bray. Nonlinear blind source separation using kernels. IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers, 2003, 14 (1), pp.228-236. 〈inria-00099523〉

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