On adaptivity of online model selection method based on multikernel adaptive filtering

Masahiro Yukawa; Ryu Ichiro Ishii

doi:10.1109/APSIPA.2013.6694329

On adaptivity of online model selection method based on multikernel adaptive filtering

Masahiro Yukawa, Ryu Ichiro Ishii

Department of Electronics and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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.

Original language	English
Title of host publication	2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOIs	https://doi.org/10.1109/APSIPA.2013.6694329
Publication status	Published - 2013
Event	2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan, Province of China Duration: 2013 Oct 29 → 2013 Nov 1

Publication series

Name	2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013

Other

Other	2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
Country/Territory	Taiwan, Province of China
City	Kaohsiung
Period	13/10/29 → 13/11/1

ASJC Scopus subject areas

Information Systems
Signal Processing

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10.1109/APSIPA.2013.6694329

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Yukawa, M., & Ishii, R. I. (2013). On adaptivity of online model selection method based on multikernel adaptive filtering. In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 [6694329] (2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013). https://doi.org/10.1109/APSIPA.2013.6694329

On adaptivity of online model selection method based on multikernel adaptive filtering. / Yukawa, Masahiro; Ishii, Ryu Ichiro.
2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. 2013. 6694329 (2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yukawa, M & Ishii, RI 2013, On adaptivity of online model selection method based on multikernel adaptive filtering. in 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013., 6694329, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013, Kaohsiung, Taiwan, Province of China, 13/10/29. https://doi.org/10.1109/APSIPA.2013.6694329

Yukawa M, Ishii RI. On adaptivity of online model selection method based on multikernel adaptive filtering. In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. 2013. 6694329. (2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013). doi: 10.1109/APSIPA.2013.6694329

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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.",

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On adaptivity of online model selection method based on multikernel adaptive filtering

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