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