Computationally efficient sparse Bayesian learning via generalized approximate message passing

Xianbing Zou, Fuwei Li, Jun Fang, Hongbin Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

The sparse Bayesian learning (also referred to as Bayesian compressed sensing) algorithm is a popular approach for sparse signal recovery, and has demonstrated superior performance in several experiments. Nevertheless, the sparse Bayesian learning algorithm has a computational complexity that grows rapidly with the dimension of the signal, which hinders its application to many practical problems even with moderately large data sets. To address this issue, in this paper, we propose a computationally efficient sparse Bayesian learning method by integrating the generalized approximate message passing (GAMP) technique. Specifically, the algorithm is developed within an expectation-maximization (EM) framework, using the GAMP to efficiently compute an approximation of the posterior distribution of hidden variables. The hyperparameters associated with the hierarchical Gaussian prior are learned by iteratively maximizing the Q-function which is calculated based on the posterior approximation obtained from the GAMP. Numerical results are provided to illustrate the computational efficiency and the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016
ISBN (Electronic)9781509013173
DOIs
StatePublished - 16 Dec 2016
Event16th IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016 - Nanjing, China
Duration: 16 Oct 201619 Oct 2016

Publication series

Name2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016

Conference

Conference16th IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016
Country/TerritoryChina
CityNanjing
Period16/10/1619/10/16

Keywords

  • Sparse Bayesian learning
  • expectation-maximization
  • generalized approximate message passing

Fingerprint

Dive into the research topics of 'Computationally efficient sparse Bayesian learning via generalized approximate message passing'. Together they form a unique fingerprint.

Cite this