Affinely constrained online learning and its application to beamforming

Konstantinos Slavakis, Sergios Theodoridis

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

1 Scopus citations

Abstract

This paper presents a novel method for incorporating a-priori affine constraints in online kernel-based learning tasks. The proposed technique elaborates the generic tool of projections to form a sequence of estimates in Reproducing Kernel Hilbert Spaces (RKHS). The method guarantees that the whole sequence of estimates lies in the given affine constraint set. To validate the algorithm, a beamforming task is considered. The numerical results show that the proposed frame provides with solutions in cases where the classical linear approach collapses, and forms proper beam-patterns as opposed to a recent unconstrained kernel-based regression method.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages1573-1576
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: Apr 19 2009Apr 24 2009

Publication series

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

Other

Other2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/TerritoryTaiwan, Province of China
CityTaipei
Period4/19/094/24/09

Keywords

  • Beamforming
  • Learning systems

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