Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering

arXiv: Statistics Theory, Volume abs/1802.04397, 2018.

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Abstract:

Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable by introducing a novel framework for clustering overfitted emph{parametric} (i.e. misspecified) mixture models. These conditions generalize existing conditions in the literature, and are flexible ...More

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