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Communication Dans Un Congrès Année : 2010

Sparse Representation Algorithms Based on Mean-Field Approximations

Résumé

In this paper we address the problem of sparse representation (SR) within a Bayesian framework. We assume that the observations are generated from a Bernoulli-Gaussian process and consider the corresponding Bayesian inference problem. Tractable solutions are then proposed based on the ''mean-field" approximation and the variational Bayes EM algorithm. The resulting SR algorithms are shown to have a tractable complexity and very good performance over a wide range of sparsity levels. In particular, they significantly improve the critical sparsity upon state-of-the-art SR algorithms.
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Dates et versions

inria-00589354 , version 1 (09-01-2012)

Identifiants

  • HAL Id : inria-00589354 , version 1

Citer

Cédric Herzet, Angélique Drémeau. Sparse Representation Algorithms Based on Mean-Field Approximations. Proc. IEEE Int'l Conf. on Acoustics, Speech and Signal Processing (ICASSP), Mar 2010, Dallas, United States. pp.2034-2037. ⟨inria-00589354⟩
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