Presentation 2013-02-01
Multikernel Adaptive Filtering With Double Regularization
Masahiro YUKAWA, Ryu-ichiro ISHII,
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Abstract(in English) 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.
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Keyword(in English) proximity operator / multiple kernels / kernel adaptive filter
Paper # SIP2012-105,RCS2012-262
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Committee SIP
Conference Date 2013/1/24(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Multikernel Adaptive Filtering With Double Regularization
Sub Title (in English)
Keyword(1) proximity operator
Keyword(2) multiple kernels
Keyword(3) kernel adaptive filter
1st Author's Name Masahiro YUKAWA
1st Author's Affiliation Dept. Electrical and Electronic Engineering, Niigata University()
2nd Author's Name Ryu-ichiro ISHII
2nd Author's Affiliation Dept. Electrical and Electronic Engineering, Niigata University
Date 2013-02-01
Paper # SIP2012-105,RCS2012-262
Volume (vol) vol.112
Number (no) 423
Page pp.pp.-
#Pages 4
Date of Issue