Random Fourier Features for Asymmetric Kernels

Abstract

Random Fourier Features for Asymmetric kernels total masses on a sub-training set, which enjoys computational efficiency in high dimensions. Our AsK-RFFs method is empirically validated on several typical large-scale datasets and achieves promising kernel approximation performance, which demonstrate the effectiveness of AsK-RFFs.