Probabilistic kernel least mean squares algorithms

Il Memming Park, Sohan Seth, Steven Van Vaerenbergh

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    6 Citations (Scopus)

    Abstract

    The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that 'kernelizes' the celebrated (linear) least mean squares algorithm. We demonstrate that the least mean squares algorithm is closely related to the Kalman filtering, and thus, the KLMS can be interpreted as an approximate Bayesian filtering method. This allows us to systematically develop extensions of the KLMS by modifying the underlying state-space and observation models. The resulting extensions introduce many desirable properties such as 'forgetting', and the ability to learn from discrete data, while retaining the computational simplicity and time complexity of the original algorithm.

    Original languageEnglish
    Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
    PublisherIEEE
    Pages8272-8276
    Number of pages5
    ISBN (Electronic) 978-1-4799-2893-4
    ISBN (Print)9781479928927
    DOIs
    Publication statusPublished - 2014
    MoE publication typeA4 Article in a conference publication
    EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Florence, Italy
    Duration: 4 May 20149 May 2014
    Conference number: 39

    Publication series

    Name IEEE International Conference on Acoustics, Speech and Signal Processing
    PublisherIEEE
    ISSN (Print)1520-6149
    ISSN (Electronic)2379-190X

    Conference

    ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
    Abbreviated titleICASSP
    CountryItaly
    CityFlorence
    Period04/05/201409/05/2014

    Keywords

    • kernel adaptive filtering
    • KLMS
    • sequential Bayesian learning
    • state-space model

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