Distribution Embeddings in Reproducing Kernel Hilbert Spaces
نویسندگان
چکیده
The “kernel trick” is well established as a means of constructing nonlinear algorithms from linear ones, by transferring the linear algorithms to a high dimensional feature space: specifically, a reproducing kernel Hilbert space (RKHS). Recently, it has become clear that a potentially more far reaching use of kernels is as a linear way of dealing with higher order statistics, by embedding probability distributions in a suitable RKHS. These representations allow us to painlessly represent high order properties of distributions, and to compare distributions in a nonparametric setting.
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