LAUSR

Abstract Kernel adaptive filters (KAFs) with growing network structures incur high computational burden. Generally, sparsification methods are introduced to curb the growth of the filter structure under some threshold rules, resulting in a variable structure. Unlike the sparsification methods, the Nystrom method uses a subset of data samples to form the filter structure of a fixed size. In this paper, to combat the large outliers efficiently, the kernel recursive maximum correntropy with Nystrom approximation (KRMC-NA) is proposed to achieve desirable filtering performance under a fixed and efficient filter structure. In addition, the theoretical analysis on the convergence characteristics of KRMC-NA is provided. Simulation results illustrate that the proposed KRMC-NA has better filtering accuracy than KAFs with sparsification, and approaches the filtering accuracy of the kernel recursive maximum correntropy using lower computational complexity.