6533b85dfe1ef96bd12be98b
RESEARCH PRODUCT
Sparse Deconvolution Using Support Vector Machines
Manel Martínez-ramónJordi Munoz-mariGustau Camps-vallsCarlos M. CruzJosé Luis Rojo-álvarezAníbal R. Figueiras-vidalsubject
Blind deconvolutionSignal processingTelecomunicacionesSparse deconvolutionSupport vector machinesDual modelsbusiness.industryComputer sciencelcsh:ElectronicsComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONlcsh:TK7800-8360Pattern recognitionSparse approximationRegularization (mathematics)lcsh:TelecommunicationSupport vector machineRobustness (computer science)lcsh:TK5101-6720Sysmology3325 Tecnología de las TelecomunicacionesArtificial intelligenceDeconvolutionbusinessDigital signal processingdescription
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. Publicado
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2008-04-03 |