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Conference papers
Regularized Kernel-Based Wiener Filtering. Application to Magnetoencephalographic Signals Denoising.
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.
https://hal-utt.archives-ouvertes.fr/hal-02861473
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Tuesday, June 9, 2020 - 8:43:52 AM
Last modification on : Friday, August 27, 2021 - 3:14:07 PM
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Tuesday, June 9, 2020 - 8:43:52 AM
Last modification on : Friday, August 27, 2021 - 3:14:07 PM
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