Hierarchical Latent Dirichlet Allocation Metamodel
Jump to navigation
Jump to search
See: Latent Dirichlet Allocation Metamodel, Hierarchical Bayesian Metamodel, MALLET System, Hierarchical Dirichlet Process.
References
2003
- (Blei, Griffiths & al) ⇒ David M. Blei, Thomas L. Griffiths, Michael I. Jordan, and Joshua B. Tenenbaum. (2003). “Hierarchical topic models and the nested Chinese restaurant process.” In: Neural Information Processing Systems 16 (NIPS 2003).
- QUOTE: We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting — which of the large collection of possible trees to use? We take a Bayesian approach, generating an appropriate prior via a distribution on partitions that we refer to as the nested Chinese restaurant process. This nonparametric prior allows arbitrarily large branching factors and readily accommodates growing data collections. We build a hierarchical topic model by combining this prior with a likelihood that is based on a hierarchical variant of latent Dirichlet allocation. We illustrate our approach on simulated data and with an application to the modeling of NIPS abstracts. 1