Support Exploration Algorithm for Sparse Support Recovery - Ecole Centrale de Marseille Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

Support Exploration Algorithm for Sparse Support Recovery

Résumé

We introduce a new algorithm promoting sparsity called {\it Support Exploration Algorithm (SEA)} and analyze it in the context of support recovery/model selection problems. The algorithm can be interpreted as an instance of the {\it straight-through estimator (STE)} applied to the resolution of a sparse linear inverse problem. SEA uses a non-sparse exploratory vector and makes it evolve in the input space to select the sparse support. We put to evidence an oracle update rule for the exploratory vector and consider the STE update. The theoretical analysis establishes general sufficient conditions of support recovery. The general conditions are specialized to the case where the matrix $A$ performing the linear measurements satisfies the {\it Restricted Isometry Property (RIP)}. Experiments show that SEA can efficiently improve the results of any algorithm. Because of its exploratory nature, SEA also performs remarkably well when the columns of $A$ are strongly coherent.
Fichier principal
Vignette du fichier
Main.pdf (2.22 Mo) Télécharger le fichier
Main.synctex.gz (291.78 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03964976 , version 1 (31-01-2023)
hal-03964976 , version 2 (07-02-2024)

Licence

Domaine public

Identifiants

  • HAL Id : hal-03964976 , version 1

Citer

Mimoun Mohamed, François Malgouyres, Valentin Emiya, Caroline Chaux. Support Exploration Algorithm for Sparse Support Recovery. 2023. ⟨hal-03964976v1⟩
155 Consultations
80 Téléchargements

Partager

Gmail Facebook X LinkedIn More