Efficient construction of dictionaries for kernel adaptive filtering in a dynamic environment

Abstract

One of the major challenges in kernel adaptive filtering is how to construct an efficient dictionary of observed input signals. In this paper, we propose novel dictionary adaptation rules for kernel adaptive filtering. The first algorithm can efficiently “move” elements of the dictionary to increase the approximation performance. The second algorithm mainly focuses on a nonstationary system, which can yield the increase of the dictionary size. The proposed method can eliminate unnecessary elements in the dictionary. Numerical examples support the efficacy of the proposed methods.