Rethinking Collapsed Variational Bayes Inference for LDA
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 α-divergence. We show that the CVB0 inference is composed of two different divergence projections: α = 1 and -1. This interpretation will help shed light on CVB0 works.
Cite
Text
Sato and Nakagawa. "Rethinking Collapsed Variational Bayes Inference for LDA." International Conference on Machine Learning, 2012.Markdown
[Sato and Nakagawa. "Rethinking Collapsed Variational Bayes Inference for LDA." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/sato2012icml-rethinking/)BibTeX
@inproceedings{sato2012icml-rethinking,
title = {{Rethinking Collapsed Variational Bayes Inference for LDA}},
author = {Sato, Issei and Nakagawa, Hiroshi},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/sato2012icml-rethinking/}
}