BAYESIAN PURSUIT ALGORITHM FOR SPARSE REPRESENTATION

被引：30

作者：

Zayyani, H. ^{[1
]}

Babaie-Zadeh, M. ^{[1
]}

Jutten, C. ^{[2
]}

机构：

[1] Sharif Univ Technol, Adv Commun Res Inst, Dept Elect Engn, Tehran, Iran

[2] GIPSA Lab, Grenoble, France

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS | 2009年

关键词：

Sparse representation; Sparse Component Analysis (SCA); Compressed Sensing (CS); Pursuit algorithms; DECOMPOSITION; NORM;

D O I：

10.1109/ICASSP.2009.4959892

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：1549 / +

页数：2

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