Sparse Deconvolution Using Support Vector Machines. (28th May 2008)

• Record Type: Journal Article
• Authors: Rojo-Álvarez Rojo-Álvarez, José Luis José Luis; Martínez-Ramón Martínez-Ramón, Manel Manel; Muñoz-Marí Muñoz-Marí, Jordi Jordi; Camps-Valls Camps-Valls, Gustavo Gustavo; Cruz Cruz, Carlos M. Carlos M.; Figueiras-Vidal Figueiras-Vidal, Aníbal R. Aníbal R.
• Other Names: Evgeniou Evgeniou Theodoros Theodoros Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2008-05-28
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/816507
• Languages: English
• ISSNs: 1687-6172

Abstract

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.