Sparse Deconvolution Using Support Vector Machines
2008; Springer Science+Business Media; Volume: 2008; Issue: 1 Linguagem: Inglês
10.1155/2008/816507
ISSN1687-6180
AutoresJosé Luis Rojo‐Álvarez, Manel Martínez‐Ramón, Jordi Muñoz-Marı́, Gustau Camps‐Valls, Carlos M. Cruz, Anı́bal R. Figueiras-Vidal,
Tópico(s)Fault Detection and Control Systems
ResumoSparse 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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