Thresholding-based online algorithms of complexity comparable to sparse LMS methods

Yannis Kopsinis, Konstantinos Slavakis, Sergios Theodoridis, Stephen McLaughlin

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

5 Citations (Scopus)

Abstract

This paper deals with a novel class of set-theoretic adaptive sparsity promoting algorithms of linear computational complexity. Sparsity is induced via generalized thersholding operators, which correspond to nonconvex penalties such as those used in a number of sparse LMS based schemes. The results demonstrate the significant performance gain of our approach, at comparable computational cost.

Original languageEnglish
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
Pages513-516
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Symposium on Circuits and Systems - Beijing, China
Duration: 19 May 201323 May 2013

Conference

Conference2013 IEEE International Symposium on Circuits and Systems
Abbreviated titleISCAS 2013
Country/TerritoryChina
CityBeijing
Period19/05/1323/05/13

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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