Name
Abstract | ||
|---|---|---|
We propose the hierarchical Dirichlet process (HDP), a nonparametric Bayesian model for clustering problems involving multiple groups of data. Each group of data is modeled with a mixture, with the number of components being open-ended and inferred automatically by the model. Further, components can be shared across groups, allowing dependencies across groups to be modeled effectively as well as conferring generaliza- tion to new groups. Such grouped clustering problems occur often in practice, e.g. in the problem of topic discovery in document corpora. We report experimental results on three text corpora showing the effective and superior performance of the HDP over previous models. |
Year | Venue | Keywords |
|---|---|---|
2004 | NIPS | hierarchical dirichlet process,bayesian model |
Field | DocType | Citations |
Hierarchical Dirichlet process,Cluster (physics),Latent Dirichlet allocation,Computer science,Nonparametric bayesian,Text corpus,Artificial intelligence,Dirichlet distribution,Cluster analysis,Machine learning | Conference | 75 |
PageRank | References | Authors |
7.90 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yee Whye Teh | 1 | 6253 | 539.26 |
| Michael I. Jordan | 2 | 31220 | 3640.80 |
| Matthew J. Beal | 3 | 600 | 64.31 |
| David M. Blei | 4 | 10843 | 818.64 |