Stagewise Weak Gradient Pursuits

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).


[img] PDF BD_SWGP.pdf - Other
Restricted to Repository staff only

Download (386kB)


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

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/TSP.2009.2025088
ISSNs: 1053-587X (print)
Related URLs:
Keywords: sparse representations/approximations, orthogonal matching pursuit, weak matching pursuit, gradient pursuit, stagewise selection, compressed sensing
Organisations: Signal Processing & Control Grp
ePrint ID: 142503
Date :
Date Event
November 2009Published
Date Deposited: 31 Mar 2010 15:02
Last Modified: 18 Apr 2017 20:07
Further Information:Google Scholar

Actions (login required)

View Item View Item