Dictionary Adaptation for Adaptive Filtering With Multiple Kernels in a Dynamic Environment

被引：0

作者：

Wada, Tomoya ^{[1
]}

Tanaka, Toshihisa ^{[1
]}

机构：

[1] Tokyo Univ Agr & Technol, Dept Elect & Elect Engn, 2-14-16 Nakacho, Koganei, Tokyo 1848588, Japan

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC) | 2016年

关键词：

nonlinear adaptive filtering; kernel methods; reproducing kernel Hilbert space; dictionary learning; HILBERT-SPACES;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

One of the major challenges in adaptive filtering is choosing an appropriate model. In this paper, we propose a novel dictionary adaptation rule with multiple kernels. The proposed method can eliminate unnecessary elements in the dictionary by using block l(1)-regularization. In addition to that, the proposed method can efficiently "move" elements of the dictionary to increase the approximation performance. Numerical examples support the efficacy of the proposed method.

引用

页数：4

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