A Unified Framework of Random Feature KLMS Algorithms and Convergence Analysis

Abstract

Random feature kernel least mean square RF KLMS) algorithms, like the random Fourier feature KLMS (RFF-KLMS), can effectively reduce the computation and storage burdens of the KLMS algorithm in the process of update. However, little work has been done to perform the convergence analysis for such algorithms. To this end, in this paper, we present a unified framework of RF-KLMS algorithms, and based on which, a universal model for convergence analysis is given. As two examples, the RFF-KLMS and the random Gaussian feature KLMS (RGF-KLMS) are discussed detailedly. Simulations demonstrate the validity of the theoretical analysis. Index Terms–Kernel least mean square, random feature, universal model, convergence analysis