A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
Abstract
Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference pro- cedures like variational Bayes and Gibbs sampling have been found lacking. In this paper we propose the collapsed variational Bayesian inference algorithm for LDA, and show that it is computationally efficient, easy to implement and signifi- cantly more accurate than standard variational Bayesian inference for LDA.
Cite
Text
Teh et al. "A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation." Neural Information Processing Systems, 2006.Markdown
[Teh et al. "A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/teh2006neurips-collapsed/)BibTeX
@inproceedings{teh2006neurips-collapsed,
title = {{A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation}},
author = {Teh, Yee W. and Newman, David and Welling, Max},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {1353-1360},
url = {https://mlanthology.org/neurips/2006/teh2006neurips-collapsed/}
}