Recovering Distributions from Gaussian RKHS Embeddings


Motonobu Kanagawa, Kenji Fukumizu ;
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:457-465, 2014.


Recent advances of kernel methods have yielded a framework for nonparametric statistical inference called RKHS embeddings, in which all probability distributions are represented as elements in a reproducing kernel Hilbert space, namely kernel means. In this paper, we consider the recovery of the information of a distribution from an estimate of the kernel mean, when a Gaussian kernel is used. To this end, we theoretically analyze the properties of a consistent estimator of a kernel mean, which is represented as a weighted sum of feature vectors. First, we prove that the weighted average of a function in a Besov space, whose weights and samples are given by the kernel mean estimator, converges to the expectation of the function. As corollaries, we show that the moments and the probability measures on intervals can be recovered from an estimate of the kernel mean. We also prove that a consistent estimator of the density of a distribution can be defined using a kernel mean estimator. This result confirms that we can in fact completely recover the information of distributions from RKHS embeddings.

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