Sparsity-aware adaptive learning: A set theoretic estimation approach. ?

S. Theodoridis, Y. Kopsinis, Konstantinos Slavakis, S. Chouvardas

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

1 Scopus citations

Abstract

This paper reviews recent advances on online/adaptive sparsity-promoting algorithms. The emphasis is on on a recent family of schemes, which build upon convex analytic tools. The benefits of this algorithmic family is that it can easily deal with the existence of a set of convex constraints and also to bypass the need of differentiability of cost functions. It can thus deal well with notions related to robustness and their associated costs. Extensions to constraints, which are realized via mappings whose fixed point set are non-convex, are also discussed. The case of learning in a distributed fashion is also discussed.

Original languageEnglish (US)
Title of host publication11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013 - Proceedings
PublisherIFAC Secretariat
Pages748-756
Number of pages9
Edition11 PART
ISBN (Print)9783902823373
DOIs
StatePublished - Jan 1 2013
Event11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013 - Caen, France
Duration: Jul 3 2013Jul 5 2013

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number11 PART
Volume46
ISSN (Print)1474-6670

Other

Other11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013
CountryFrance
CityCaen
Period7/3/137/5/13

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    Theodoridis, S., Kopsinis, Y., Slavakis, K., & Chouvardas, S. (2013). Sparsity-aware adaptive learning: A set theoretic estimation approach. ? In 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2013 - Proceedings (11 PART ed., pp. 748-756). (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 46, No. 11 PART). IFAC Secretariat. https://doi.org/10.3182/20130703-3-FR-4038.00157