Online learning based on iterative projections in sum space of linear and Gaussian reproducing kernel Hilbert spaces

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

1 Citation (Scopus)

Abstract

We propose a novel multikernel adaptive filtering algorithm based on the iterative projections in the sum space of reproducing kernel Hilbert spaces. We employ linear and Gaussian kernels, envisioning an application to partially-linear-system identification/estimation. The algorithm is derived by reformulating the hyperplane projection along affine subspace (HYPASS) algorithm in the sum space. The projection is computable by virtue of Minh's theorem proved in 2010 as long as the input space has nonempty interior. Numerical examples show the efficacy of the proposed algorithm.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3362-3366
Number of pages5
Volume2015-August
ISBN (Print)9781467369978
DOIs
Publication statusPublished - 2015 Aug 4
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 2014 Apr 192014 Apr 24

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period14/4/1914/4/24

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Keywords

  • multikernel adaptive filtering
  • orthogonal projection
  • reproducing kernel Hilbert space
  • sum space

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Yukawa, M. (2015). Online learning based on iterative projections in sum space of linear and Gaussian reproducing kernel Hilbert spaces. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 2015-August, pp. 3362-3366). [7178594] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2015.7178594