Support Vector Machines Framework for Linear Signal Processing

This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minim...

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Authors: Rojo-Álvarez, José Luis, Camps Valls, Gustavo, Martínez Ramón, Manel, Soria Olivas, Emilio, Navia Vázquez, Ángel, Figueiras Vidal, Aníbal R
Format: article
Publication Date:2009
Country:España
Institution:Universidad Rey Juan Carlos
Repository:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/2489
Online Access:http://hdl.handle.net/10115/2489
Access Level:Open access
Keyword:Telecomunicaciones
3325 Tecnología de las Telecomunicaciones
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spelling Support Vector Machines Framework for Linear Signal ProcessingRojo-Álvarez, José LuisCamps Valls, GustavoMartínez Ramón, ManelSoria Olivas, EmilioNavia Vázquez, ÁngelFigueiras Vidal, Aníbal RTelecomunicaciones3325 Tecnología de las TelecomunicacionesThis paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of Infinite Impulse Response filters using the gamma structure, and complex ARMA models for communication applications. The good performance shown on these different domains suggests that other signal processing problems can be stated from this SVM framework.Teoría de la Señal y Comunicaciones200920092009info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10115/2489reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/24892026-06-24T12:48:17Z
dc.title.none.fl_str_mv Support Vector Machines Framework for Linear Signal Processing
title Support Vector Machines Framework for Linear Signal Processing
spellingShingle Support Vector Machines Framework for Linear Signal Processing
Rojo-Álvarez, José Luis
Telecomunicaciones
3325 Tecnología de las Telecomunicaciones
title_short Support Vector Machines Framework for Linear Signal Processing
title_full Support Vector Machines Framework for Linear Signal Processing
title_fullStr Support Vector Machines Framework for Linear Signal Processing
title_full_unstemmed Support Vector Machines Framework for Linear Signal Processing
title_sort Support Vector Machines Framework for Linear Signal Processing
dc.creator.none.fl_str_mv Rojo-Álvarez, José Luis
Camps Valls, Gustavo
Martínez Ramón, Manel
Soria Olivas, Emilio
Navia Vázquez, Ángel
Figueiras Vidal, Aníbal R
author Rojo-Álvarez, José Luis
author_facet Rojo-Álvarez, José Luis
Camps Valls, Gustavo
Martínez Ramón, Manel
Soria Olivas, Emilio
Navia Vázquez, Ángel
Figueiras Vidal, Aníbal R
author_role author
author2 Camps Valls, Gustavo
Martínez Ramón, Manel
Soria Olivas, Emilio
Navia Vázquez, Ángel
Figueiras Vidal, Aníbal R
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Telecomunicaciones
3325 Tecnología de las Telecomunicaciones
topic Telecomunicaciones
3325 Tecnología de las Telecomunicaciones
description This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of Infinite Impulse Response filters using the gamma structure, and complex ARMA models for communication applications. The good performance shown on these different domains suggests that other signal processing problems can be stated from this SVM framework.
publishDate 2009
dc.date.none.fl_str_mv 2009
2009
2009
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10115/2489
url http://hdl.handle.net/10115/2489
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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