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Published in:
2007 | OriginalPaper | Chapter
A Hilbert Space Embedding for Distributions
Authors : Alex Smola, Arthur Gretton, Le Song, Bernhard Schölkopf
Published in: Algorithmic Learning Theory
Publisher: Springer Berlin Heidelberg
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We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.