Rethinking Collapsed Variational Bayes Inference for LDA
Machine Learning
2012-07-03 v1 Machine Learning
Abstract
We propose a novel interpretation of the collapsed variational Bayes inference with a zero-order Taylor expansion approximation, called CVB0 inference, for latent Dirichlet allocation (LDA). We clarify the properties of the CVB0 inference by using the alpha-divergence. We show that the CVB0 inference is composed of two different divergence projections: alpha=1 and -1. This interpretation will help shed light on CVB0 works.
Cite
@article{arxiv.1206.6435,
title = {Rethinking Collapsed Variational Bayes Inference for LDA},
author = {Issei Sato and Hiroshi Nakagawa},
journal= {arXiv preprint arXiv:1206.6435},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)