Skip to main content

Sparse Deconvolution Using Support Vector Machines

Abstract

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.

Publisher note

To access the full article, please see PDF.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to JoséLuis Rojo-Álvarez.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Rojo-Álvarez, J., Martínez-Ramón, M., Muñoz-Marí, J. et al. Sparse Deconvolution Using Support Vector Machines. EURASIP J. Adv. Signal Process. 2008, 816507 (2008). https://doi.org/10.1155/2008/816507

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2008/816507

Keywords