Online model-selection and learning for nonlinear estimation based on multikernel adaptive filtering

Osamu Toda, Masahiro Yukawa

研究成果: Article査読

6 被引用数 (Scopus)

抄録

We study a use of Gaussian kernels with a wide range of scales for nonlinear function estimation. The estimation task can then be split into two sub-tasks: (i) model selection and (ii) learning (parameter estimation) under the selected model. We propose a fully-adaptive and all-in-one scheme that jointly carries out the two sub-tasks based on the multikernel adaptive filtering framework. The task is cast as an asymptotic minimization problem of an instantaneous fidelity function penalized by two types of block l1-norm regularizers. Those regularizers enhance the sparsity of the solution in two different block structures, leading to effi- cient model selection and dictionary refinement. The adaptive generalized forward-backward splitting method is derived to deal with the asymptotic minimization problem. Numerical examples show that the scheme achieves the model selection and learning simultaneously, and demonstrate its strik- ing advantages over the multiple kernel learning (MKL) method called SimpleMKL.

本文言語English
ページ(範囲)236-250
ページ数15
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E100A
1
DOI
出版ステータスPublished - 2017 1月

ASJC Scopus subject areas

  • 信号処理
  • 応用数学
  • 電子工学および電気工学
  • コンピュータ グラフィックスおよびコンピュータ支援設計

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