Bayesian Pursuit algorithm for sparse representation

Author

Zayyani, H. ; Babaie-Zadeh, M. ; Jutten, C.

Author_Institution

Dept. of Electr. Eng. & Adv. Commun., Sharif Univ. of Technol., Tehran

fYear

2009

fDate

19-24 April 2009

Firstpage

1549

Lastpage

1552

Abstract

In this paper, we propose a Bayesian pursuit algorithm for sparse representation. It uses both the simplicity of the pursuit algorithms and optimal Bayesian framework to determine active atoms in sparse representation of a signal. We show that using Bayesian Hypothesis testing to determine the active atoms from the correlations leads to an efficient activity measure. Simulation results show that our suggested algorithm has better performance among the algorithms which have been implemented in our simulations in most of the cases.

Keywords

Bayes methods; correlation methods; signal representation; sparse matrices; statistical testing; Bayesian hypothesis testing; Bayesian pursuit algorithm; active atom; correlation method; sparse matrix; sparse signal representation; Atomic measurements; Bayesian methods; Collaborative work; Compressed sensing; Dictionaries; Iterative algorithms; Matching pursuit algorithms; Pursuit algorithms; Signal processing algorithms; Sparse matrices; Compressed Sensing (CS); Pursuit algorithms; Sparse Component Analysis (SCA); Sparse representation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on

Conference_Location

Taipei

ISSN

1520-6149

Print_ISBN

978-1-4244-2353-8

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2009.4959892

Filename

4959892