Generalized thresholding sparsity-aware algorithm for low complexity online learning

Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis, Steve McLaughlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

In this paper, a novel scheme for online, sparsity-aware learning is presented. A new theory is developed that allows for the incorporation, in a unifying way, of different thresholding rules to promote sparsity, that may even be of a nonconvex nature. The complexity of the algorithm exhibits a linear dependence on the number of free parameters.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages3277-3280
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period3/25/123/30/12

Keywords

  • Adaptive filtering
  • signal recovery
  • sparsity
  • thresholding operators

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