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Sparse Deconvolution Using Support Vector Machines

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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.

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Correspondence to JoséLuis Rojo-Álvarez.

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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.

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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) doi:10.1155/2008/816507

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Keywords

  • Support Vector Machine
  • Information Technology
  • Signal Processing
  • Deconvolution
  • Digital Signal