Kernel Weights for Equalizing Kernel-Wise Convergence Rates of Multikernel Adaptive Filtering
Summary :
Multikernel adaptive filtering is an attractive nonlinear approach to online estimation/tracking tasks. Despite its potential advantages over its single-kernel counterpart, a use of inappropriately weighted kernels may result in a negligible performance gain. In this paper, we propose an efficient recursive kernel weighting technique for multikernel adaptive filtering to activate all the kernels. The proposed weights equalize the convergence rates of all the corresponding partial coefficient errors. The proposed weights are implemented via a certain metric design based on the weighting matrix. Numerical examples show, for synthetic and multiple real datasets, that the proposed technique exhibits a better performance than the manually-tuned kernel weights, and that it significantly outperforms the online multiple kernel regression algorithm.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.6 pp.927-939
- Publication Date
- 2021/06/01
- Publicized
- 2020/12/11
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020EAP1080
- Type of Manuscript
- PAPER
- Category
- Algorithms and Data Structures
Authors
Kwangjin JEONG
Keio University
Masahiro YUKAWA
Keio University
Keyword
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Kwangjin JEONG, Masahiro YUKAWA, "Kernel Weights for Equalizing Kernel-Wise Convergence Rates of Multikernel Adaptive Filtering" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 6, pp. 927-939, June 2021, doi: 10.1587/transfun.2020EAP1080.
Abstract: Multikernel adaptive filtering is an attractive nonlinear approach to online estimation/tracking tasks. Despite its potential advantages over its single-kernel counterpart, a use of inappropriately weighted kernels may result in a negligible performance gain. In this paper, we propose an efficient recursive kernel weighting technique for multikernel adaptive filtering to activate all the kernels. The proposed weights equalize the convergence rates of all the corresponding partial coefficient errors. The proposed weights are implemented via a certain metric design based on the weighting matrix. Numerical examples show, for synthetic and multiple real datasets, that the proposed technique exhibits a better performance than the manually-tuned kernel weights, and that it significantly outperforms the online multiple kernel regression algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAP1080/_p
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@ARTICLE{e104-a_6_927,
author={Kwangjin JEONG, Masahiro YUKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Kernel Weights for Equalizing Kernel-Wise Convergence Rates of Multikernel Adaptive Filtering},
year={2021},
volume={E104-A},
number={6},
pages={927-939},
abstract={Multikernel adaptive filtering is an attractive nonlinear approach to online estimation/tracking tasks. Despite its potential advantages over its single-kernel counterpart, a use of inappropriately weighted kernels may result in a negligible performance gain. In this paper, we propose an efficient recursive kernel weighting technique for multikernel adaptive filtering to activate all the kernels. The proposed weights equalize the convergence rates of all the corresponding partial coefficient errors. The proposed weights are implemented via a certain metric design based on the weighting matrix. Numerical examples show, for synthetic and multiple real datasets, that the proposed technique exhibits a better performance than the manually-tuned kernel weights, and that it significantly outperforms the online multiple kernel regression algorithm.},
keywords={},
doi={10.1587/transfun.2020EAP1080},
ISSN={1745-1337},
month={June},}
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TY - JOUR
TI - Kernel Weights for Equalizing Kernel-Wise Convergence Rates of Multikernel Adaptive Filtering
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 927
EP - 939
AU - Kwangjin JEONG
AU - Masahiro YUKAWA
PY - 2021
DO - 10.1587/transfun.2020EAP1080
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2021
AB - Multikernel adaptive filtering is an attractive nonlinear approach to online estimation/tracking tasks. Despite its potential advantages over its single-kernel counterpart, a use of inappropriately weighted kernels may result in a negligible performance gain. In this paper, we propose an efficient recursive kernel weighting technique for multikernel adaptive filtering to activate all the kernels. The proposed weights equalize the convergence rates of all the corresponding partial coefficient errors. The proposed weights are implemented via a certain metric design based on the weighting matrix. Numerical examples show, for synthetic and multiple real datasets, that the proposed technique exhibits a better performance than the manually-tuned kernel weights, and that it significantly outperforms the online multiple kernel regression algorithm.
ER -
English
