Generalized Thresholding Sparsity-Aware Online Learning in a Union of Subspaces

Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis, Stephen McLaughlin

Research output: Contribution to conferencePaper

7 Citations (Scopus)

Abstract

This paper studies a non-convexly constrained, sparse inverse problem in time-varying environments from a set theoretic estimation perspective. A new theory is developed that allows for the incorporation, in a unifying way, of different thresholding rules to promote sparsity, that may be even related to non-convex penalty functions. The resulted generalized thresholding operator is embodied in an efficient online, sparsity-aware learning scheme. The algorithm is of low computational complexity exhibiting a linear dependence on the number of free parameters. A convergence analysis of the proposed algorithm is conducted, and extensive experiments are also exhibited in order to validate the novel methodology.
Original languageEnglish
Pages3277 - 3280
DOIs
Publication statusPublished - 2012
Event37th IEEE International Conference on Acoustics, Speech and Signal Processing 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Conference

Conference37th IEEE International Conference on Acoustics, Speech and Signal Processing 2012
Abbreviated titleICASSP 2012
CountryJapan
CityKyoto
Period25/03/1230/03/12

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