Social Sparsity! Neighborhood Systems Enrich Structured Shrinkage Operators

Abstract : Sparse and structured signal expansions on dictionaries can be obtained through explicit modeling in the coefficient domain. The originality of the present article lies in the construction and the study of generalized shrinkage operators, whose goal is to identify structured significance maps and give rise to structured thresholding. These generalize Group Lasso and the previously introduced Elitist Lasso by introducing more flexibility in the coefficient domain modeling, and lead to the notion of social sparsity. The proposed operators are studied theoretically and embedded in iterative thresholding algorithms. Moreover, a link between these operators and a convex functional is established. Numerical studies on both simulated and real signals confirm the benefits of such an approach.
Type de document :
Article dans une revue
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2013, 61 (10), pp.2498 - 2511. 〈10.1109/TSP.2013.2250967〉
Liste complète des métadonnées

Littérature citée [51 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-00691774
Contributeur : Matthieu Kowalski <>
Soumis le : mardi 21 mai 2013 - 14:45:54
Dernière modification le : samedi 8 septembre 2018 - 16:24:02
Document(s) archivé(s) le : mardi 4 avril 2017 - 06:14:14

Fichier

KSD_SocialSparsity.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Matthieu Kowalski, Kai Siedenburg, Monika Dörfler. Social Sparsity! Neighborhood Systems Enrich Structured Shrinkage Operators. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2013, 61 (10), pp.2498 - 2511. 〈10.1109/TSP.2013.2250967〉. 〈hal-00691774v3〉

Partager

Métriques

Consultations de la notice

817

Téléchargements de fichiers

718