Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks

Ban Sok Shin, Masahiro Yukawa, Renato Calvacante, Armin Dekorsy

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)


    We propose an adaptive scheme for distributed learning of nonlinear functions by a network of nodes. The proposed algorithm consists of a local adaptation stage utilizing multiple kernels with projections onto hyperslabs and a diffusion stage to achieve consensus on the estimates over the whole network. Multiple kernels are incorporated to enhance the approximation of functions with several high and low frequency components common in practical scenarios. We provide a thorough convergence analysis of the proposed scheme based on the metric of the Cartesian product of multiple reproducing kernel Hilbert spaces. To this end, we introduce a modified consensus matrix considering this specific metric and prove its equivalence to the ordinary consensus matrix. Besides, the use of hyperslabs enables a significant reduction of the computational demand with only a minor loss in the performance. Numerical evaluations with synthetic and real data are conducted showing the efficacy of the proposed algorithm compared to the state of the art schemes.

    Original languageEnglish
    JournalIEEE Transactions on Signal Processing
    Publication statusAccepted/In press - 2018 Aug 30


    • Adaptive learning
    • consensus
    • Convergence
    • Dictionaries
    • Distributed adaptive learning
    • Indexes
    • Kernel
    • kernel adaptive filter
    • Measurement
    • multiple kernels
    • nonlinear regression
    • Signal processing algorithms
    • spatial reconstruction

    ASJC Scopus subject areas

    • Signal Processing
    • Electrical and Electronic Engineering

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