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  • Research Article
  • Open Access

Sparse Deconvolution Using Support Vector Machines

  • 1Email author,
  • 2,
  • 3,
  • 3,
  • 2 and
  • 2
EURASIP Journal on Advances in Signal Processing20082008:816507

  • Received: 26 September 2007
  • Accepted: 17 March 2008
  • Published:


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.


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

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Authors’ Affiliations

Department of Signal Theory and Communications, Universidad Rey Juan Carlos, Camino del Molino s/n, 28943 Fuenlabrada, Madrid, Spain
Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Avda Universidad, 30, 28911 Leganés, Madrid, Spain
Departament d'Enginyeria Electrònica, Universitat de València, 46010 València, Spain


© José Luis Rojo-Álvarez et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.