NESTED SPARSE BAYESIAN LEARNING FOR BLOCK-SPARSE SIGNALS WITH INTRA-BLOCK CORRELATION
Data(s) |
2014
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Resumo |
In this work, we address the recovery of block sparse vectors with intra-block correlation, i.e., the recovery of vectors in which the correlated nonzero entries are constrained to lie in a few clusters, from noisy underdetermined linear measurements. Among Bayesian sparse recovery techniques, the cluster Sparse Bayesian Learning (SBL) is an efficient tool for block-sparse vector recovery, with intra-block correlation. However, this technique uses a heuristic method to estimate the intra-block correlation. In this paper, we propose the Nested SBL (NSBL) algorithm, which we derive using a novel Bayesian formulation that facilitates the use of the monotonically convergent nested Expectation Maximization (EM) and a Kalman filtering based learning framework. Unlike the cluster-SBL algorithm, this formulation leads to closed-form EMupdates for estimating the correlation coefficient. We demonstrate the efficacy of the proposed NSBL algorithm using Monte Carlo simulations. |
Formato |
application/pdf |
Identificador |
http://eprints.iisc.ernet.in/50609/1/int_con_aco_spe_sig_pro_2014.pdf Prasad, Ranjitha and Murthy, Chandra R and Rao, Bhaskar D (2014) NESTED SPARSE BAYESIAN LEARNING FOR BLOCK-SPARSE SIGNALS WITH INTRA-BLOCK CORRELATION. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 04-09, 2014, Florence, ITALY. |
Publicador |
IEEE |
Relação |
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6854994 http://eprints.iisc.ernet.in/50609/ |
Palavras-Chave | #Electrical Communication Engineering |
Tipo |
Conference Proceedings NonPeerReviewed |