Publication:
Sparse deconvolution using support vector machines

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URI: https://hdl.handle.net/10016/11684
DOI: 10.1155/2008/816507
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816507.pdf (1.75 MB)
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2008
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Hindawi Publishing Corporation
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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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Sparse deconvolution, Sysmology, Support vector machines, Dual models
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EURASIP Journal of Advances in Signal Processing, Special Issue on Emerging Machine Learning Techniques in Signal Processing, Vol. 2008, pp. 1-13
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