Blei/The Discrete Infinite Logistic Normal Distribution 31

Abstract: We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN generalizes the hierarchical Dirichlet process (HDP) to model 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 derive a variational inference algorithm for approximate posterior inference. We apply DILN to topic modeling of documents and study its empirical performance on four corpora, comparing performance with the HDP and the correlated topic model (CTM). To compute with large-scale data, we also develop a stochastic variational inference algorithm for DILN and compare with similar algorithms for HDP and LDA on a collection of 350, 000 articles from Nature. 1