Multikernel Adaptive Filtering With Double Regularization
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概要
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We propose an efficient multikernel adaptive filtering algorithm with double regularizers. One is a block l1 norm for kernel groups which contributes to selecting relevant kernels adaptively from many possible kernels employed, preventing a nonlinear filter from overfitting noisy data. The other regularizer is a block l1 norm for data groups which contributes to updating the dictionary adaptively. As the resulting cost function contains two nonsmooth (but proximable) terms, we approximate the latter regularizer by its Moreau envelope and apply the adaptive proximal forward-backward splitting method to the approximated cost function. Numerical examples show the efficacy of the proposed algorithm.
- 一般社団法人電子情報通信学会の論文
- 2013-01-24
著者
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Yukawa Masahiro
Dept. Communications And Integrated Systems Tokyo Institute Of Technology
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ISHII Ryu-ichiro
Dept. Electrical and Electronic Engineering, Niigata University
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