Gradient pursuit for non-linear sparse signal modelling

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


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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.

Item Type: Conference or Workshop Item (Paper)
Related URLs:
Divisions : University Structure - Pre August 2011 > School of Mathematics
Faculty of Engineering and the Environment > Institute of Sound and Vibration Research > Signal Processing & Control Research Group
ePrint ID: 151911
Accepted Date and Publication Date:
April 2008Published
Date Deposited: 13 May 2010 09:00
Last Modified: 31 Mar 2016 13:24

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