Sparse Deconvolution Using Support Vector Machines

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.

• Publication year

  2008

• Venue

  EURASIP Journal on Advances in Signal Processing

• Publication date

  2008-04-03

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1155/2008/816507
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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