TY - GEN
T1 - Computationally efficient sparse Bayesian learning via generalized approximate message passing
AU - Zou, Xianbing
AU - Li, Fuwei
AU - Fang, Jun
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/16
Y1 - 2016/12/16
N2 - 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.
AB - 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.
KW - Sparse Bayesian learning
KW - expectation-maximization
KW - generalized approximate message passing
UR - http://www.scopus.com/inward/record.url?scp=85011072084&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011072084&partnerID=8YFLogxK
U2 - 10.1109/ICUWB.2016.7790383
DO - 10.1109/ICUWB.2016.7790383
M3 - Conference contribution
AN - SCOPUS:85011072084
T3 - 2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016
BT - 2016 IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016
T2 - 16th IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2016
Y2 - 16 October 2016 through 19 October 2016
ER -