Title :

Democratic prior for anti-sparse coding

Author(s) :

Elvira, Clément [Auteur]
Signal et Communications [IRIT-SC]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chainais, Pierre [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Dobigeon, Nicolas [Auteur]
Institut National Polytechnique (Toulouse) [Toulouse INP]
Signal et Communications [IRIT-SC]

Conference title :

IEEE Workshop on statistical signal processing (SSP 2016)

City :

Palma de Mallorca

Country :

Espagne

Start date of the conference :

2016-06-26

Book title :

Proceedings of IEEE SSP 2016

Publication date :

2016

English keyword(s) :

Democratic distribution
Inverse problem
Anti-sparse representation

HAL domain(s) :

Informatique [cs]/Synthèse d'image et réalité virtuelle [cs.GR]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement des images [eess.IV]
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

Anti-sparse coding aims at spreading the information uniformly over representation coefficients and can be naturally expressed through an ℓ∞-norm regularization. This paper derives a probabilistic formulation of such a ...
Show more >Anti-sparse coding aims at spreading the information uniformly over representation coefficients and can be naturally expressed through an ℓ∞-norm regularization. This paper derives a probabilistic formulation of such a problem. A new probability distribution is introduced. This so-called democratic distribution is then used as a prior to promote anti-sparsity in a linear Gaussian inverse problem. A Gibbs sampler is designed to generate samples asymptotically distributed according to the joint posterior distribution of interest. To scale to higher dimension, a proximal Markov chain Monte Carlo algorithm is proposed as an alternative to Gibbs sampling. Simulations on synthetic data illustrate the performance of the proposed method for anti-sparse coding on a complete dictionary. Results are compared with the recent deterministic variational FITRA algorithm.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL