Learning annotated hierarchies from relational data

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

Bibtex Metadata Paper


Daniel M. Roy, Charles Kemp, Vikash Mansinghka, Joshua Tenenbaum


The objects in many real-world domains can be organized into hierarchies, where each internal node picks out a category of objects. Given a collection of fea- tures and relations defined over a set of objects, an annotated hierarchy includes a specification of the categories that are most useful for describing each individual feature and relation. We define a generative model for annotated hierarchies and the features and relations that they describe, and develop a Markov chain Monte Carlo scheme for learning annotated hierarchies. We show that our model discov- ers interpretable structure in several real-world data sets.