Gradient pursuit for non-linear sparse signal modelling
Gradient pursuit for non-linear sparse signal modelling
In this paper the linear sparse signal model is extended to allow more general, non-linear relationships and more general measures of approximation error. A greedy gradient based strategy is presented
to estimate the sparse coefficients. This algorithm can be
understood as a generalisation of the recently introduced Gradient Pursuit framework. Using the presented approach with the traditional linear model but with a different cost function is shown to outperform OMP in terms of recovery of the original sparse coefficients. A second set of experiments then shows that for the nonlinear
model studied and for highly sparse signals, recovery is still possible in at least a percentage of cases.
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, Mike E.
9ca3625e-5b14-4f1f-90ac-1af468f521ae
April 2008
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, Mike E.
9ca3625e-5b14-4f1f-90ac-1af468f521ae
Blumensath, Thomas and Davies, Mike E.
(2008)
Gradient pursuit for non-linear sparse signal modelling.
European Signal Processing Conference, Lausanne, Switzerland.
24 - 28 Aug 2008.
5 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
In this paper the linear sparse signal model is extended to allow more general, non-linear relationships and more general measures of approximation error. A greedy gradient based strategy is presented
to estimate the sparse coefficients. This algorithm can be
understood as a generalisation of the recently introduced Gradient Pursuit framework. Using the presented approach with the traditional linear model but with a different cost function is shown to outperform OMP in terms of recovery of the original sparse coefficients. A second set of experiments then shows that for the nonlinear
model studied and for highly sparse signals, recovery is still possible in at least a percentage of cases.
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Published date: April 2008
Venue - Dates:
European Signal Processing Conference, Lausanne, Switzerland, 2008-08-24 - 2008-08-28
Organisations:
Signal Processing & Control Grp
Identifiers
Local EPrints ID: 151911
URI: http://eprints.soton.ac.uk/id/eprint/151911
PURE UUID: 947a69c4-37b4-4d6d-88b1-05f8a37c5b88
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Date deposited: 13 May 2010 09:00
Last modified: 24 Mar 2022 02:39
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Contributors
Author:
Mike E. Davies
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