Joint Decomposition of Multichannel Signals with Multimarginal Optimal Transport

The joint decomposition of a sequence of signals is addressed, where each signal is coded within a predefined dictionary. We propose a convex variational formulation involving a regularization term based on optimal transport. The latter is designed so as to promote proximity between neighboring channels. Two kinds of regularization criteria are designed, corresponding to distinct cross-channel information on the transport of dictionary atoms from one channel to another. The resulting optimization problems are reformulated as quadratic programs and solved using a proximal algorithm. The proposed approach is analyzed for a multichannel deconvolution problem.

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Publication Details

Published
2024-08-26
DOI
https://doi.org/10.23919/eusipco63174.2024.10715143
Primary Topic
Blind Source Separation Techniques
Type
article
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article

Joint Decomposition of Multichannel Signals with Multimarginal Optimal Transport

Charles Soussen, Jérôme Idier, Xiangyi Li, Jean Malléjac
Blind Source Separation Techniques
article

Joint Decomposition of Multichannel Signals with Multimarginal Optimal Transport

Charles Soussen, Jérôme Idier, Xiangyi Li, Jean Malléjac
article en

Abstract

The joint decomposition of a sequence of signals is addressed, where each signal is coded within a predefined dictionary. We propose a convex variational formulation involving a regularization term based on optimal transport. The latter is designed so as to promote proximity between neighboring channels. Two kinds of regularization criteria are designed, corresponding to distinct cross-channel information on the transport of dictionary atoms from one channel to another. The resulting optimization problems are reformulated as quadratic programs and solved using a proximal algorithm. The proposed approach is analyzed for a multichannel deconvolution problem.

École Centrale de Nantes (FR), Centre National de la Recherche Scientifique (FR), CentraleSupélec (FR), Laboratoire des Sciences du Numérique de Nantes (FR), Institut Fresnel (FR), Centrale Méditerranée (FR), Nantes Université (FR)
Openalex Percentile: Top 98%
Blind Source Separation Techniques
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