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Stagewise Weak Gradient Pursuits

Record type: Article

Finding sparse solutions to underdetermined inverse problems is a fundamental challenge encountered in a wide range of signal processing applications, from signal acquisition to source separation. This paper looks at greedy algorithms that are applicable to very large problems. The main contribution is the development of a new selection strategy (called stagewise weak selection) that effectively selects several elements in each iteration. The new selection strategy is based on the realization that many classical proofs for recovery of sparse signals can be trivially extended to the new setting. What is more, simulation studies show the computational benefits and good performance of the approach. This strategy can be used in several greedy algorithms, and we argue for the use within the gradient pursuit framework in which selected coefficients are updated using a conjugate update direction. For this update, we present a fast implementation and novel convergence result

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Citation

Blumensath, T. and Davies, M.E. (2009) Stagewise Weak Gradient Pursuits IEEE Transactions on Signal Processing, 57, (11), pp. 4333-4346. (doi:10.1109/TSP.2009.2025088).

More information

Published date: November 2009
Keywords: sparse representations/approximations, orthogonal matching pursuit, weak matching pursuit, gradient pursuit, stagewise selection, compressed sensing
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 142503
URI: http://eprints.soton.ac.uk/id/eprint/142503
ISSN: 1053-587X
PURE UUID: b6375ccc-ae29-47e8-aab9-dca0c88ca1bc

Catalogue record

Date deposited: 31 Mar 2010 15:02
Last modified: 18 Jul 2017 23:11

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