The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling

Notable paper award

John Paisley, Chong Wang, David Blei; JMLR W&CP 15:74-82, 2011.

Abstract

We present the discrete infinite logistic normal distribution (DILN, ""Dylan""), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representation of DILN as a normalized collection of gamma-distributed random variables, and study its statistical properties. We consider applications to topic modeling and derive a variational Bayes algorithm for approximate posterior inference. We study the empirical performance of the DILN topic model on four corpora, comparing performance with the HDP and the correlated topic model.

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