OpenAlex | A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging

A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging

Work

Year: 2018

Type: article

Abstract: Majorization-minimization (MM) is a standard iterative optimization technique which consists in minimizing a sequence of convex surrogate functionals. MM approaches have been particularly successful t... more

Source: Inverse Problems

Authors Yousra Bekhti, Felix Lucka, Joseph Salmon, Alexandre Gramfort

Institutions Université Paris-Saclay, Laboratoire Traitement et Communication de l’Information, Télécom Paris, Centrum Wiskunde & Informatica, University College London +2 more

Cites: 60

Cited by: 14

Related to: 10

FWCI: 2.482

Citation percentile (by year/subfield): 77.52

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Funder Agence Nationale de la Recherche

Grant ID ANR-14-NEUC-0002-01