Seeking The Truly Correlated Topic Posterior - on tight approximate inference of logistic-normal admixture model

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Amr Ahmed, Eric P. Xing ;
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:19-26, 2007.

Abstract

The Logistic-Normal Topic Admixture Model (LoNTAM), also known as correlated topic model (Blei and Lafferty, 2005), is a promising and expressive admixture-based text model. It can capture topic correlations via the use of a logistic-normal distribution to model non-trivial variabilities in the topic mixing vectors underlying documents. However, the non-conjugacy caused by the logistic-normal makes posterior inference and model learning significantly more challenging. In this paper, we present a new, tight approximate inference algorithm for LoNTAM based on a multivariate quadratic Taylor approximation scheme that facilitates elegant closed-form message passing. We present experimental results on simulated data as well as on the NIPS17 and PNAS document collections, and show that our approach is not only simple and easy to implement, but also it converges faster, and leads to more accurate recovery of the semantic truth underlying documents and estimates of the parameters comparing to previous methods.

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