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Regularized Kernel-Based Wiener Filtering. Application to Magnetoencephalographic Signals Denoising.

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Abstract

We take a new approach in nonlinear Wiener filtering. This approach is based on the theory of reproducing kernel Hilbert spaces (RKHS). By means of the well-known "kernel trick", the arithmetic operations are carried out in the initial space. We show that the solution is given by solving a linear system which may be ill-conditioned. To find a solution for such a problem, we resorted to a kernel principal component analysis (KPCA) method to perform dimensionality reduction in RKHS. A new reduced-rank Wiener filter based on KPCA is thus elaborated. It is applied on magnetoencephalographic (MEG) data for cardiac artifacts extraction.
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hal-02861473 , version 1 (09-06-2020)

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Ibtissam Constantin, Cédric Richard, Régis Lengellé, Laurent Soufflet. Regularized Kernel-Based Wiener Filtering. Application to Magnetoencephalographic Signals Denoising.. (ICASSP '05) IEEE International Conference on Acoustics, Speech, and Signal Processing 2005, Mar 2005, Philadelphia, United States. pp.289-292, ⟨10.1109/ICASSP.2005.1416002⟩. ⟨hal-02861473⟩
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