抄録
This paper exemplifies that the use of multiple kernels leads to efficient adaptive filtering for nonlinear systems. Two types of multikernel adaptive filtering algorithms are proposed. One is a simple generalization of the kernel normalized least mean square (KNLMS) algorithm , adopting a coherence criterion for dictionary designing. The other is derived by applying the adaptive proximal forward-backward splitting method to a certain squared distance function plus a weighted block l 1 norm penalty, encouraging the sparsity of an adaptive filter at the block level for efficiency. The proposed multikernel approach enjoys a higher degree of freedom than those approaches which design a kernel as a convex combination of multiple kernels. Numerical examples show that the proposed approach achieves significant gains particularly for nonstationary data as well as insensitivity to the choice of some design-parameters.
| 元の言語 | English |
|---|---|
| 記事番号 | 6203609 |
| ページ（範囲） | 4672-4682 |
| ページ数 | 11 |
| ジャーナル | IEEE Transactions on Signal Processing |
| 巻 | 60 |
| 発行部数 | 9 |
| DOI | |
| 出版物ステータス | Published - 2012 |
| 外部発表 | Yes |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing
これを引用
Multikernel adaptive filtering. / Yukawa, Masahiro.
：: IEEE Transactions on Signal Processing, 巻 60, 番号 9, 6203609, 2012, p. 4672-4682.研究成果: Article
}
TY - JOUR
T1 - Multikernel adaptive filtering
AU - Yukawa, Masahiro
PY - 2012
Y1 - 2012
N2 - This paper exemplifies that the use of multiple kernels leads to efficient adaptive filtering for nonlinear systems. Two types of multikernel adaptive filtering algorithms are proposed. One is a simple generalization of the kernel normalized least mean square (KNLMS) algorithm , adopting a coherence criterion for dictionary designing. The other is derived by applying the adaptive proximal forward-backward splitting method to a certain squared distance function plus a weighted block l 1 norm penalty, encouraging the sparsity of an adaptive filter at the block level for efficiency. The proposed multikernel approach enjoys a higher degree of freedom than those approaches which design a kernel as a convex combination of multiple kernels. Numerical examples show that the proposed approach achieves significant gains particularly for nonstationary data as well as insensitivity to the choice of some design-parameters.
AB - This paper exemplifies that the use of multiple kernels leads to efficient adaptive filtering for nonlinear systems. Two types of multikernel adaptive filtering algorithms are proposed. One is a simple generalization of the kernel normalized least mean square (KNLMS) algorithm , adopting a coherence criterion for dictionary designing. The other is derived by applying the adaptive proximal forward-backward splitting method to a certain squared distance function plus a weighted block l 1 norm penalty, encouraging the sparsity of an adaptive filter at the block level for efficiency. The proposed multikernel approach enjoys a higher degree of freedom than those approaches which design a kernel as a convex combination of multiple kernels. Numerical examples show that the proposed approach achieves significant gains particularly for nonstationary data as well as insensitivity to the choice of some design-parameters.
KW - Block soft-thresholding operator
KW - kernel adaptive filtering
KW - reproducing kernel Hilbert space
UR - http://www.scopus.com/inward/record.url?scp=84865211015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865211015&partnerID=8YFLogxK
U2 - 10.1109/TSP.2012.2200889
DO - 10.1109/TSP.2012.2200889
M3 - Article
AN - SCOPUS:84865211015
VL - 60
SP - 4672
EP - 4682
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 9
M1 - 6203609
ER -