Learning Topic Models: Identifiability and Finite-Sample Analysis. (arXiv:2110.04232v2 [stat.ML] UPDATED)

Topic models provide a useful text-mining tool for learning, extracting, and
discovering latent structures in large text corpora. Although a plethora of
methods have been proposed for topic modeling, lacking in the literature is a
formal theoretical investigation of the statistical identifiability and
accuracy of latent topic estimation. In this paper, we propose a maximum
likelihood estimator (MLE) of latent topics based on a specific integrated
likelihood that is naturally connected to the concept, in computational
geometry, of volume minimization. …

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