TY - GEN
T1 - Sparse Bayesian dictionary learning with a Gaussian hierarchical model
AU - Yang, Linxiao
AU - Fang, Jun
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/5/18
Y1 - 2016/5/18
N2 - We consider a dictionary learning problem aimed at designing a dictionary such that the signals admits a sparse or an approximate sparse representation over the learned dictionary. The problem finds a variety of applications including image denoising, feature extraction, etc. In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation. Suitable non-informative priors are also placed on the dictionary and the noise variance such that they can be reliably estimated from the data. Based on the hierarchical model, a Gibbs sampling method is developed for Bayesian inference. The proposed method have the advantage that it does not require the knowledge of the noise variance a priori. Numerical results show that the proposed method is able to learn the dictionary with an accuracy better than existing methods.
AB - We consider a dictionary learning problem aimed at designing a dictionary such that the signals admits a sparse or an approximate sparse representation over the learned dictionary. The problem finds a variety of applications including image denoising, feature extraction, etc. In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation. Suitable non-informative priors are also placed on the dictionary and the noise variance such that they can be reliably estimated from the data. Based on the hierarchical model, a Gibbs sampling method is developed for Bayesian inference. The proposed method have the advantage that it does not require the knowledge of the noise variance a priori. Numerical results show that the proposed method is able to learn the dictionary with an accuracy better than existing methods.
KW - Dictionary learning
KW - Gaussian-inverse Gamma prior
KW - Gibbs sampling
UR - http://www.scopus.com/inward/record.url?scp=84973338488&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973338488&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2016.7472140
DO - 10.1109/ICASSP.2016.7472140
M3 - Conference contribution
AN - SCOPUS:84973338488
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2564
EP - 2568
BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Y2 - 20 March 2016 through 25 March 2016
ER -