Learning Annotated Hierarchies from Relational Data
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 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.
Cite
Text
Roy et al. "Learning Annotated Hierarchies from Relational Data." Neural Information Processing Systems, 2006.Markdown
[Roy et al. "Learning Annotated Hierarchies from Relational Data." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/roy2006neurips-learning/)BibTeX
@inproceedings{roy2006neurips-learning,
title = {{Learning Annotated Hierarchies from Relational Data}},
author = {Roy, Daniel M. and Kemp, Charles and Mansinghka, Vikash K. and Tenenbaum, Joshua B.},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {1185-1192},
url = {https://mlanthology.org/neurips/2006/roy2006neurips-learning/}
}