- Research Article
- Open Access
Sparse Deconvolution Using Support Vector Machines
© José Luis Rojo-Álvarez et al. 2008
Received: 26 September 2007
Accepted: 17 March 2008
Published: 3 April 2008
Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing is presented and analyzed, including comparative evaluations of its performance from the points of view of estimation and detection capabilities, and of robustness with respect to non-Gaussian additive noise.
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