Online coordinate descent for adaptive estimation of sparse signals

Daniele Angelosante, Juan Andres Bazerque, Georgios B. Giannakis

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

6 Scopus citations

Abstract

Two low-complexity sparsity-aware recursive schemes are developed for real-time adaptive signal processing. Both rely on a novel online coordinate descent algorithm which minimizes a time-weighted least-squares cost penalized with the scaled ℓ1 norm of the unknown parameters. In addition to computational savings offered when processing time-invariant sparse parameter vectors, both schemes can be used for tracking slowly varying sparse signals. Analysis and preliminary simulations confirm that when the true signal is sparse the proposed estimators converge to a time-weighted least-absolute shrinkage and selection operator, and both outperform sparsity-agnostic recursive least-squares alternatives.

Original languageEnglish (US)
Title of host publication2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Pages369-372
Number of pages4
DOIs
StatePublished - Dec 25 2009
Event2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 - Cardiff, United Kingdom
Duration: Aug 31 2009Sep 3 2009

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
CountryUnited Kingdom
CityCardiff
Period8/31/099/3/09

Keywords

  • Basis pursuit
  • Compressive sensing
  • Coordinate descent
  • Lasso
  • Recursive least-squares

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