Sparse Representation Algorithms Based on Mean-Field Approximations

Cedric Herzet 1 Angélique Drémeau 2
2 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : 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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Communication dans un congrès
Proc. IEEE Int'l Conf. on Acoustics, Speech and Signal Processing (ICASSP), Mar 2010, Dallas, United States. pp.2034-2037, 2010
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Cedric 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, 2010. 〈inria-00589354〉

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