Sparsity-promoting adaptive algorithm for distributed learning in diffusion networks

Symeon Chouvardas, Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis

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

2 Scopus citations

Abstract

In this paper, a sparsity-promoting adaptive algorithm for distributed learning in diffusion networks is developed. The algorithm follows the set-theoretic estimation rationale, i.e., at each time instant and at each node, a closed convex set, namely a hyperslab, is constructed around the current measurement point. This defines the region in which the solution lies. The algorithm seeks a solution in the intersection of these hyperslabs by a sequence of projections. Sparsity is encouraged in two complimentary ways: a) by employing extra projections onto a weighted ℓ1 ball, that complies with our desire to constrain the respective weighted ℓ1 norm and b) by adopting variable metric projections onto the hyperslabs, which implicitly quantify data mismatch. A combine-adapt cooperation strategy is adopted. Under some mild assumptions, the scheme enjoys a number of elegant convergence properties. Finally, numerical examples verify the validity of the proposed scheme, compared to other algorithms, which have been developed in the context of sparse adaptive learning.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Pages1084-1088
Number of pages5
StatePublished - Nov 27 2012
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: Aug 27 2012Aug 31 2012

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference20th European Signal Processing Conference, EUSIPCO 2012
CountryRomania
CityBucharest
Period8/27/128/31/12

Keywords

  • Adaptive distributed learning
  • diffusion networks
  • projections
  • sparsity

Fingerprint Dive into the research topics of 'Sparsity-promoting adaptive algorithm for distributed learning in diffusion networks'. Together they form a unique fingerprint.

Cite this