The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
다중 커널 적응 필터링은 온라인 추정/추적 작업에 대한 매력적인 비선형 접근 방식입니다. 단일 커널에 비해 잠재적인 이점이 있음에도 불구하고 가중치가 부적절하게 부여된 커널을 사용하면 성능이 거의 향상되지 않을 수 있습니다. 본 논문에서는 모든 커널을 활성화하기 위한 다중 커널 적응 필터링을 위한 효율적인 재귀 커널 가중치 기법을 제안한다. 제안된 가중치는 해당하는 모든 부분 계수 오류의 수렴 속도를 균등화합니다. 제안된 가중치는 가중치 행렬을 기반으로 한 특정 메트릭 설계를 통해 구현됩니다. 수치적 예는 합성 및 다중 실제 데이터 세트에 대해 제안된 기법이 수동으로 조정된 커널 가중치보다 더 나은 성능을 나타내며 온라인 다중 커널 회귀 알고리즘보다 훨씬 뛰어난 성능을 나타냄을 보여줍니다.
Kwangjin JEONG
Keio University
Masahiro YUKAWA
Keio University
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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
부
@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},}
부
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 -