DocumentCode :
661467
Title :
On adaptivity of online model selection method based on multikernel adaptive filtering
Author :
Yukawa, Masahiro ; Ishii, Ryu-ichiro
Author_Institution :
Dept. of Electron. & Electr. Eng., Keio Univ., Yokohama, Japan
fYear :
2013
fDate :
Oct. 29 2013-Nov. 1 2013
Firstpage :
1
Lastpage :
6
Abstract :
We investigate adaptivity of the online model selection method which has been proposed recently within the multikernel adaptive filtering framework. Specifically, we consider a situation in which the nonlinear system under study changes during adaptation and an appropriate kernel also does accordingly. Our time-varying cost functions involve three regularizers: the ℓ1 norm and two block ℓ1 norms which promote sparsity both in the kernel and data groups. The block ℓ1 regularizers are approximated by their Moreau envelopes, and the adaptive proximal forward-backward splitting (APFBS) method is applied to the approximated cost function. Numerical examples show that the proposed algorithm can adaptively estimate a reasonable model.
Keywords :
adaptive estimation; adaptive filters; approximation theory; APFBS method; Moreau envelope; adaptive proximal forward-backward splitting method; block ℓ1 norm; multikernel adaptive filtering framework; nonlinear system; online model selection method; time-varying cost function; Adaptation models; Cost function; Dictionaries; Indexes; Kernel; Nonlinear systems; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific
Conference_Location :
Kaohsiung
Type :
conf
DOI :
10.1109/APSIPA.2013.6694329
Filename :
6694329
Link To Document :
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