A Hilbert Space Embedding for Distributions
Alex Smola, Arthur Gretton, Le Song and Bernhard Schölkopf
Algorithmic Learning Theory
Lecture Notes in Computer Science
, Sendai, Japan
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.