Generalized iterative thresholding for sparsity-aware online volterra system identification

Konstantinos Slavakis, Yannis Kopsinis, Sergios Theodoridis, Georgios B Giannakis, Vassilis Kekatos

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

Abstract

The present paper explores the link between thresholding, one of the key enablers in sparsity-promoting algorithms, and Volterra system identification in the context of time-adaptive or online learning. A connection is established between the recently developed generalized thresholding operator and optimization theory via the concept of proximalmappings which are associated with non-convex penalizing functions. Based on such a variational analytic ground, two iterative thresholding algorithms are provided for the sparsity-cognizant Volterra system identification task: (i) a set theoretic estimation one by using projections onto hyperslabs, and (ii) a Landweber-type one. Numerical experimentation is provided to validate the proposed algorithms with respect to state-ofthe- Art, sparsity-aware online learning techniques.

Original languageEnglish (US)
Title of host publication10th International Symposium on Wireless Communication Systems 2013, ISWCS 2013
PublisherIEEE Computer Society
Pages180-184
Number of pages5
ISBN (Print)9783800735297
StatePublished - Jan 1 2013
Event10th IEEE International Symposium on Wireless Communication Systems 2013, ISWCS 2013 - Ilmenau, Germany
Duration: Aug 27 2013Aug 30 2013

Publication series

NameProceedings of the International Symposium on Wireless Communication Systems
ISSN (Print)2154-0217
ISSN (Electronic)2154-0225

Other

Other10th IEEE International Symposium on Wireless Communication Systems 2013, ISWCS 2013
CountryGermany
CityIlmenau
Period8/27/138/30/13

Keywords

  • Adaptive filtering
  • Proximal mapping
  • Sparsity
  • Thresholding
  • Volterra

Fingerprint Dive into the research topics of 'Generalized iterative thresholding for sparsity-aware online volterra system identification'. Together they form a unique fingerprint.

Cite this