Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering

12 Feb 2018 Bryon Aragam Chen Dan Eric P. Xing Pradeep Ravikumar

Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable, by introducing a novel framework involving clustering overfitted \emph{parametric} (i.e. misspecified) mixture models. These identifiability conditions generalize existing conditions in the literature, and are flexible enough to include for example mixtures of Gaussian mixtures... (read more)

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