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Correlation-aware techniques for sparse support recovery

Pal, Piya and Vaidyanathan, P. P. (2012) Correlation-aware techniques for sparse support recovery. In: 2012 IEEE Statistical Signal Processing Workshop. IEEE , Piscataway, NJ, pp. 53-56. ISBN 978-1-4673-0182-4.

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Sparse support recovery techniques guarantee successful recovery of sparse solutions to linear underdetermined systems provided the measurement matrix satisfies certain conditions. The maximum level of sparsity that can be recovered with existing algorithms is O(M) where M denotes the size of the measurement vector. This paper shows how this can be improved to O(M^2) by assuming certain prior knowledge about the correlation structure of the measurements. The theory for correlation aware framework for support recovery is developed, which involves the Khatri-Rao (KR) product of the measurement matrix. Necessary and sufficient conditions for unique recovery of the sparse support are provided for this new framework which outperforms the more traditional CS techniques in terms of required size of the measurement vector. It also gives rise to interesting questions of constructing classes of measurement matrices which can exploit the prior correlation knowledge in an effective way.

Item Type:Book Section
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Vaidyanathan, P. P.0000-0003-3003-7042
Additional Information:© 2012 IEEE. Date of Current Version: 04 October 2012. Work supported in parts by the ONR grant N00014-11-1-0676, and the California Institute of Technology.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-11-1-0676
Record Number:CaltechAUTHORS:20121011-143607324
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Official Citation:Pal, Piya; Vaidyanathan, P. P.; , "Correlation-aware techniques for sparse support recovery," Statistical Signal Processing Workshop (SSP), 2012 IEEE , vol., no., pp.53-56, 5-8 Aug. 2012 doi: 10.1109/SSP.2012.6319753 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:34857
Deposited By: Tony Diaz
Deposited On:11 Oct 2012 21:43
Last Modified:09 Nov 2021 23:11

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