Adaptive nonlinear estimation based on parallel projection along Affine subspaces in reproducing kernel Hilbert space

Masa Aki Takizawa, Masahiro Yukawa

    Research output: Contribution to journalArticle

    21 Citations (Scopus)

    Abstract

    We propose a novel algorithm using a reproducing kernel for adaptive nonlinear estimation. The proposed algorithm is based on three ideas: projection-along-subspace, selective update, and parallel projection. The projection-along-subspace yields excellent performances with small dictionary sizes. The selective update effectively reduces the complexity without any serious degradation of performance. The parallel projection leads to fast convergence/tracking accompanied by noise robustness. A convergence analysis in the non-selective-update case is presented by using the adaptive projected subgradient method. Simulation results exemplify the benefits from the three ideas as well as showing the advantages over the state-of-the-art algorithms. The proposed algorithm bridges the quantized kernel least mean square algorithm of Chen and the sparse sequential algorithm of Dodd et al.

    Original languageEnglish
    Article number7112637
    Pages (from-to)4257-4269
    Number of pages13
    JournalIEEE Transactions on Signal Processing
    Volume63
    Issue number16
    DOIs
    Publication statusPublished - 2015 Aug 15

    Keywords

    • Convex projection
    • kernel adaptive filtering
    • reproducing kernel Hilbert space

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Signal Processing

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