Kernel Mean Embedding of Distributions: A Review and Beyond
نویسندگان
چکیده
منابع مشابه
Kernel Mean Embedding of Distributions: A Review and Beyond
A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has recently emerged as a powerful tool for machine learning and 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 origi...
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A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n sequences with s anomalous sequences to be detected. Each typical sequence contains m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence containsm i.i.d. samples drawn from a distribution q that is distinct from p. The distribution...
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Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert space, called kernel embedding of distributions. In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings. The proposed method is applied to state-space models where sampling from the transition model is possible, while the observation model ...
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ژورنال
عنوان ژورنال: Foundations and Trends® in Machine Learning
سال: 2017
ISSN: 1935-8237,1935-8245
DOI: 10.1561/2200000060