A Polarized Random Fourier Feature Kernel Least-Mean-Square Algorithm

Abstract

This paper presents a polarized random Fourier feature kernel least-mean-square algorithm that aims to overcome the dimension curve of the random Fourier feature kernel least-mean-square (RFFKLMS) algorithm. RFFKLMS is an effective nonlinear adaptive filtering algorithm based on the kernel approximation technique. However, random samples drawn from the distribution need more dimensions to achieve better-generalized performance because they are independent of the training data. To overcome this weakness, a kernel polarization method is adopted to optimize the random samples. Polarized random Fourier features demonstrate a clear advantage over a method without using the polarization method. The experimental results in the context of Lorenz time series prediction and channel equalization verify the effectiveness of the proposed method.