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Journal Articles IEEE Transactions on Neural Networks Year : 2003

Nonlinear blind source separation using kernels

Dominique Martinez
Alistair Bray
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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.
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inria-00099523 , version 1 (26-09-2006)


  • HAL Id : inria-00099523 , version 1


Dominique Martinez, Alistair Bray. Nonlinear blind source separation using kernels. IEEE Transactions on Neural Networks, 2003, 14 (1), pp.228-236. ⟨inria-00099523⟩
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