A Polarized Random Fourier Feature Kernel Least-Mean-Square Algorithm
Authors:
Open Access
- 4 April 2019
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 50833-50838
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. Experimental results in the context of Lorenz time series prediction and channel equalization verify the effectiveness of the proposed method.Keywords
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