Matching Pursuits with Random Sequential Subdictionaries - IMT - Institut Mines-Télécom Accéder directement au contenu
Article Dans Une Revue Signal Processing Année : 2012

Matching Pursuits with Random Sequential Subdictionaries

Résumé

Matching pursuits are a class of greedy algorithms commonly used in signal processing, for solving the sparse approximation problem. They rely on an atom selection step that requires the calculation of numerous projections, which can be computationally costly for large dictionaries and burdens their competitiveness in coding applications. We propose using a non adaptive random sequence of subdictionaries in the decomposition process, thus parsing a large dictionary in a probabilistic fashion with no additional projection cost nor parameter estimation. A theoretical modeling based on order statistics is provided, along with experimental evidence showing that the novel algorithm can be efficiently used on sparse approximation problems. An application to audio signal compression with multiscale time-frequency dictionaries is presented, along with a discussion of the complexity and practical implementations.
Fichier principal
Vignette du fichier
Moussallam2012_RSSMP.pdf (691.58 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00696181 , version 1 (11-05-2012)

Identifiants

Citer

Manuel Moussallam, Laurent Daudet, Gael Richard. Matching Pursuits with Random Sequential Subdictionaries. Signal Processing, 2012, 92, pp.2532-2544. ⟨10.1016/j.sigpro.2012.03.019⟩. ⟨hal-00696181⟩
172 Consultations
443 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More