The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling
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.
Cite
Text
Paisley et al. "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[Paisley et al. "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/paisley2011aistats-discrete/)BibTeX
@inproceedings{paisley2011aistats-discrete,
title = {{The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling}},
author = {Paisley, John and Wang, Chong and Blei, David},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {74-82},
volume = {15},
url = {https://mlanthology.org/aistats/2011/paisley2011aistats-discrete/}
}