Distinguishing distributions with interpretable features
international conference on machine learning(2016)
摘要
Two semimetrics on probability distributions are
proposed, based on a difference between features
chosen from each, where these features can be in
either the spatial or Fourier domains. The features are chosen so as to maximize the distinguishability of the distributions, by optimizing
a lower bound of power for a statistical test using these features. The result is a parsimonious
and interpretable indication of how and where
two distributions differ, which can be used even
in high dimensions, and when the difference is
localized in the Fourier domain. A real-world
benchmark image data demonstrates that the returned features provide a meaningful and informative indication as to how the distributions differ
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