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.
Full text not available from this repository.
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)|
|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
|Date Deposited:||13 May 2010 09:00|
|Last Modified:||27 Mar 2014 19:11|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)