NESTED SPARSE BAYESIAN LEARNING FOR BLOCK-SPARSE SIGNALS WITH INTRA-BLOCK CORRELATION

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.

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Abstract

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.

Item Type:	Conference Proceedings
Series.:	International Conference on Acoustics Speech and Signal Processing ICASSP
Publisher:	IEEE
Additional Information:	Copyright for this article belongs to the IEEE, USA
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	12 Jan 2015 06:58
Last Modified:	12 Jan 2015 06:58
URI:	http://eprints.iisc.ac.in/id/eprint/50609

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