Name
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 Gibb 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 significantly more accurate than standard variational Bayesian inference for LDA. |
Year | Venue | Keywords |
|---|---|---|
2006 | NIPS | computer vision,gibbs sampling,bayesian network,latent dirichlet allocation |
Field | DocType | Citations |
Frequentist inference,Latent Dirichlet allocation,Bayesian inference,Computer science,Inference,Algorithm,Artificial intelligence,Statistical inference,Bayesian statistics,Machine learning,Gibbs sampling,Variational message passing | Conference | 171 |
PageRank | References | Authors |
12.09 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yee Whye Teh | 1 | 6253 | 539.26 |
| David Newman | 2 | 1319 | 73.72 |
| Max Welling | 3 | 4875 | 550.34 |