Distributed adaptive learning with multiple kernels in diffusion networks

Ban Sok Shin, Masahiro Yukawa, Renato Luis Garrido Cavalcante, Armin Dekorsy

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

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- A nd 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
Article number8453003
Pages (from-to)5505-5519
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume66
Issue number21
DOIs
Publication statusPublished - 2018 Nov 1

Keywords

  • Distributed adaptive learning
  • consensus
  • kernel adaptive filter
  • multiple kernels
  • nonlinear regression
  • spatial reconstruction

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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