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A kernel approach to comparing distributions

Gretton, A; Borgwardt, KM; Rasch, M; Schölkopf, B; Smola, AJ; (2007) A kernel approach to comparing distributions. In: UNSPECIFIED (pp. 1637-1641).

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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. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Type: Book chapter
Title: A kernel approach to comparing distributions
ISBN: 1577353234
URI: http://discovery.ucl.ac.uk/id/eprint/1334331
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