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Gradient pursuit for non-linear sparse signal modelling

Blumensath, Thomas and Davies, Mike E. (2008) Gradient pursuit for non-linear sparse signal modelling At European Signal Processing Conference, Switzerland. 25 - 29 Aug 2008. 5 pp.

Record type: Conference or Workshop Item (Paper)


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, Switzerland, 2008-08-25 - 2008-08-29
Organisations: Signal Processing & Control Grp


Local EPrints ID: 151911
PURE UUID: 947a69c4-37b4-4d6d-88b1-05f8a37c5b88

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Date deposited: 13 May 2010 09:00
Last modified: 18 Jul 2017 12:55

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