Learning distributed jointly sparse systems by collaborative LMS

Yuantao Gu, Mengdi Wang

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

1 Scopus citations

Abstract

In the proposed model of adaptive filtering network, distributed learning algorithm works cooperatively to identify separated unknown systems, which have different impulse responses. Specifically, JS-CoLMS algorithm is proposed to iteratively learn the unknown systems and the joint sparsity, based on a stochastic gradient approach and a subdifferentiable sparse-inducing penalty approximating the l2,0 norm. The superior performance of the proposed algorithm and its relation to l0-LMS and Leaky LMS are briefly discussed and verified by numerical experiments.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7228-7232
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

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

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Collaborative LMS
  • Distributed learning
  • Distributed optimization
  • JS-CoLMS
  • Leaky LMS
  • adaptive filtering network
  • joint sparsity

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