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OLS-R1: estimation parcimonieuse de sources cérébrales par régression itérative sous contrainte de rang

Abstract : This paper introduces a new sparse algorithm from the iterative regression family. Unlike the classical OLS, the elements of the dictionary are not vectors but matrices. Moreover, the (vectorial) regression coefficients representing time varying amplitudes are constrained to unit rank. The target application is the inverse problem in brain source estimation. On simulated data, the proposed algorithm shows better performances than classical solutions used for solving the mentioned inverse problem.
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https://hal-cnrs.archives-ouvertes.fr/hal-03780800
Contributor : Radu Ranta Connect in order to contact the contributor
Submitted on : Monday, September 19, 2022 - 4:50:32 PM
Last modification on : Wednesday, September 28, 2022 - 4:53:09 AM

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  • HAL Id : hal-03780800, version 1

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Radu Ranta, Steven Le Cam. OLS-R1: estimation parcimonieuse de sources cérébrales par régression itérative sous contrainte de rang. XXVIIIème Colloque Francophone de Traitement du Signal et des Images, GRETSI 2022, Sep 2022, Nancy, France. ⟨hal-03780800⟩

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