On Estimation and Selection for Topic Models

Details On Estimation and Selection for Topic Models On Estimation and Selection for Topic Models

2011-09-21

arxiv.org/abs/1109.4518v3

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Scheduled to appear in the proceedings of AISTATS 2012

Scientific paper

This article describes posterior maximization for topic models, identifying computational and conceptual gains from inference under a non-standard parametrization. We then show that fitted parameters can be used as the basis for a novel approach to marginal likelihood estimation, via block-diagonal approximation to the information matrix,that facilitates choosing the number of latent topics. This likelihood-based model selection is complemented with a goodness-of-fit analysis built around estimated residual dispersion. Examples are provided to illustrate model selection as well as to compare our estimation against standard alternative techniques.

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