Adaptive algorithm for sparse system identification using projections onto weighted ℓ1 balls

Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis

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

31 Scopus citations

Abstract

This paper presents a novel projection-based adaptive algorithm for sparse system identification. Sequentially observed data are used to generate an equivalent number of closed convex sets, namely hyperslabs, which quantify an associated cost criterion. Sparsity is exploited by the introduction of appropriately designed weighted ℓ1 balls. The algorithm uses only projections onto hyperslabs and weighted ℓ1 balls, and results into a computational complexity of order O(L) multiplications/additions and O(Llog2 L) sorting operations, where L is the length of the system to be estimated. Numerical results are also given to validate the proposed method against very recently developed sparse LMS and RLS type of algorithms, which are considered to belong to the same type of algorithmic family.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages3742-3745
Number of pages4
DOIs
StatePublished - Nov 8 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

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

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • Adaptive filtering
  • Projections
  • Sparsity

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