DocumentCode :
2504610
Title :
Soft Bayesian pursuit algorithm for sparse representations
Author :
Drémeau, Angélique ; Herzet, Cédric ; Daudet, Laurent
Author_Institution :
Inst. Langevin, Univ Paris Diderot, Paris, France
fYear :
2011
fDate :
28-30 June 2011
Firstpage :
341
Lastpage :
344
Abstract :
This paper deals with sparse representations within a Bayesian framework. For a Bernoulli-Gaussian model, we here propose a method based on a mean-field approximation to estimate the support of the signal. In numerical tests involving a recovery problem, the resulting algorithm is shown to have good performance over a wide range of sparsity levels, compared to various state-of-the-art algorithms.
Keywords :
Bayes methods; Gaussian processes; signal representation; Bernoulli-Gaussian model; mean-field approximation; recovery problem; signal representation; soft Bayesian pursuit algorithm; sparse representation; Approximation algorithms; Approximation methods; Bayesian methods; Matching pursuit algorithms; Noise; Signal processing algorithms; Strontium; Bernoulli-Gaussian model; Sparse representations; mean-field approximation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2011 IEEE
Conference_Location :
Nice
ISSN :
pending
Print_ISBN :
978-1-4577-0569-4
Type :
conf
DOI :
10.1109/SSP.2011.5967699
Filename :
5967699
Link To Document :
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