• English
    • français
  • Help
  •  | 
  • Contact
  •  | 
  • About
  •  | 
  • Login
  • HAL portal
  •  | 
  • Pages Pro
  • EN
  •  / 
  • FR
View Item 
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Democratic prior for anti-sparse coding
  • BibTeX
  • CSV
  • Excel
  • RIS

Document type :
Communication dans un congrès avec actes
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
Files
Thumbnail
  • https://hal.archives-ouvertes.fr/hal-01433632v2/document
  • Open access
  • Access the document
Thumbnail
  • https://hal.archives-ouvertes.fr/hal-01433632v2/document
  • Open access
  • Access the document
Université de Lille

Mentions légales
Université de Lille © 2017