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Conference Papers Year : 2016

Democratic prior for anti-sparse coding

Abstract

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.
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Dates and versions

hal-01433632 , version 1 (12-01-2017)
hal-01433632 , version 2 (03-04-2017)

Identifiers

  • HAL Id : hal-01433632 , version 2
  • OATAO : 17064

Cite

Clément Elvira, Pierre Chainais, Nicolas Dobigeon. Democratic prior for anti-sparse coding. IEEE Workshop on statistical signal processing (SSP 2016), Jun 2016, Palma de Mallorca, Spain. pp. 1-5. ⟨hal-01433632v2⟩
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