Conference Proceedings

Learning annotated hierarchies from relational data

DM Roy, C Kemp, VK Mansinghka, JB Tenenbaum

Advances in Neural Information Processing Systems | Published : 2007

Abstract

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 features 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 discovers interpretable structure in several real-world data sets.

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