Kernel Mean Embedding of Distributions

Kernel Mean Embedding of Distributions

A Review and Beyond

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A Hilbert space embedding of a distribution-in short, a kernel mean embedding-has recently emerged as a powerful tool for machine learning and statistical inference. The basic idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RKHS) in which the whole arsenal of kernel methods can be extended to probability measures. It can be viewed as a generalization of the original "feature map" common to support vector machines (SVMs) and other kernel methods. In addition to the classical applications of kernel methods, the kernel mean embedding has found novel app...