Distributed adaptive learning with multiple kernels in diffusion networks

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

研究成果: Article査読

19 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号8453003
ページ(範囲)5505-5519
ページ数15
ジャーナルIEEE Transactions on Signal Processing
66
21
DOI
出版ステータスPublished - 2018 11月 1

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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