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EURASIP Journal on Advances in Signal Processing
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Sparse Deconvolution Using Support Vector Machines

EURASIP Journal on Advances in Signal Processing volume 2008, Article number: 816507 (2008) Cite this article

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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Authors and Affiliations

  1. Department of Signal Theory and Communications, Universidad Rey Juan Carlos, Camino del Molino s/n, 28943, Fuenlabrada, Madrid, Spain

    JoséLuis Rojo-Álvarez

  2. Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Avda Universidad, 30, 28911, Leganés, Madrid, Spain

    Manel Martínez-Ramón, Carlos M. Cruz & Aníbal R. Figueiras-Vidal

  3. Departament d'Enginyeria Electrònica, Universitat de València, 46010, València, Spain

    Jordi Muñoz-Marí & Gustavo Camps-Valls

Authors
  1. JoséLuis Rojo-Álvarez
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  2. Manel Martínez-Ramón
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  3. Jordi Muñoz-Marí
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  4. Gustavo Camps-Valls
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  5. Carlos M. Cruz
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  6. Aníbal R. Figueiras-Vidal
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Corresponding author

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). https://doi.org/10.1155/2008/816507

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

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