A Hilbert Space Embedding for Distributions
(invited lecture for ALT 2007)
Authors: Alexander J. Smola, Arthur Gretton, Karsten Borgwardt, Le Song, and Bernhard Schölkopf
Affiliation: Machine Learning Program Leader,
National ICT Australia / ANU, Canberra, Australia.
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.
©Copyright 2007 Authors