TY - GEN
T1 - Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals
AU - Shen, Yanning
AU - Duan, Huiping
AU - Fang, Jun
AU - Li, Hongbin
PY - 2014
Y1 - 2014
N2 - In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. A pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, where a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm.
AB - In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. A pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, where a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm.
KW - Sparse Bayesian learning
KW - block-sparse signal recovery
KW - pattern-coupled hierarchical model
UR - http://www.scopus.com/inward/record.url?scp=84905230327&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905230327&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6853928
DO - 10.1109/ICASSP.2014.6853928
M3 - Conference contribution
AN - SCOPUS:84905230327
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1896
EP - 1900
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -