TY - GEN
T1 - Support knowledge-aided sparse Bayesian learning for compressed sensing
AU - Fang, Jun
AU - Shen, Yanning
AU - Li, Fuwei
AU - Li, Hongbin
AU - Chen, Zhi
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/4
Y1 - 2015/8/4
N2 - In this paper, we study the problem of sparse signal recovery when partial but partly erroneous prior knowledge of the signal's support is available. Based on the conventional sparse Bayesian learning framework, we propose an improved hierarchical prior model. The proposed modeling constitutes a three-layer hierarchical form. The first two layers, similar to the conventional sparse Bayesian learning, place a Gaussian-inverse-Gamma prior on the signal, while the third layer is newly added, with a prior placed on the parameters {bi}, where {bi} are parameters characterizing the sparsity-controlling hyperparameters {αi}. Such a modeling enables to automatically learn the true support from partly erroneous information through learning the values of the parameters {bi}. A variational Bayesian inference algorithm is developed based on the proposed prior model. Numerical results are provided to illustrate the performance of the proposed algorithm.
AB - In this paper, we study the problem of sparse signal recovery when partial but partly erroneous prior knowledge of the signal's support is available. Based on the conventional sparse Bayesian learning framework, we propose an improved hierarchical prior model. The proposed modeling constitutes a three-layer hierarchical form. The first two layers, similar to the conventional sparse Bayesian learning, place a Gaussian-inverse-Gamma prior on the signal, while the third layer is newly added, with a prior placed on the parameters {bi}, where {bi} are parameters characterizing the sparsity-controlling hyperparameters {αi}. Such a modeling enables to automatically learn the true support from partly erroneous information through learning the values of the parameters {bi}. A variational Bayesian inference algorithm is developed based on the proposed prior model. Numerical results are provided to illustrate the performance of the proposed algorithm.
KW - Compressed sensing
KW - prior support knowledge
KW - sparse Bayesian learning
UR - http://www.scopus.com/inward/record.url?scp=84946045174&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946045174&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2015.7178679
DO - 10.1109/ICASSP.2015.7178679
M3 - Conference contribution
AN - SCOPUS:84946045174
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3786
EP - 3790
BT - 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
T2 - 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Y2 - 19 April 2014 through 24 April 2014
ER -