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Journal Publications
Citation key | DKS01 |
---|---|
Author | B.-S. Shin and M. Yukawa and R. L. G. Cavalcante and A. Dekorsy |
Pages | 5505-5519 |
Year | 2018 |
Journal | IEEE Transactions on Signal Processing |
Volume | 66 |
Number | 21 |
Month | Nov. |
Note | article in a journal |
Editor | IEEE |
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. |