CUED Publications database

Learning annotated hierarchies from relational data

Roy, DM and Kemp, C and Mansinghka, VK and Tenenbaum, JB (2007) Learning annotated hierarchies from relational data. Advances in Neural Information Processing Systems. pp. 1185-1192. ISSN 1049-5258

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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.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron job
Date Deposited: 04 Feb 2015 22:04
Last Modified: 01 May 2015 19:06
DOI: