EFFICIENT SPARSE BAYESIAN LEARNING VIA GIBBS SAMPLING

被引：8

作者：

Tan, Xing ^{[1
]}

Li, Jian ^{[1
]}

Stoica, Peter ^{[2
]}

机构：

[1] Univ Florida, Dept Elect & Comp Engn, Gainesville, FL 32611 USA

[2] Uppsala Univ, Dept Informat Technol, S-75105 Uppsala, Sweden

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2010年

基金：

欧洲研究理事会; 美国国家科学基金会; 瑞典研究理事会;

关键词：

Compressed Sensing; Sparse Bayesian Learning; Gibbs Sampling; INVERSE PROBLEMS;

D O I：

10.1109/ICASSP.2010.5495896

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Sparse Bayesian learning (SBL) has been used as a signal recovery algorithm for compressed sensing. It has been shown that SBL is easy to use and can recover sparse signals more accurately than the well-known Basis Pursuit (BP) algorithm. However, the computational complexity of SBL is quite high, which limits its use in large-scale problems. We propose herein an efficient Gibbs sampling approach, referred to as GS-SBL, for compressed sensing. Numerical examples show that GS-SBL can be faster and perform better than the existing SBL approaches.

引用

页码：3634 / 3637

页数：4

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