Hierarchical Latent Class Models for Cluster Analysis
Abstract
Latent class models are used for cluster analysis of categorical data. Underlying such a model is the assumption that the observed variables are mutually independent given the class variable. A serious problem with the use of latent class models, known as local dependence, is that this assumption is often untrue. In this paper we propose hierarchical latent class models as a framework where the local dependence problem can be addressed in a principled manner. We develop a search-based algorithm for learning hierarchical latent class models from data. The algorithm is evaluated using both synthetic and real-world data.
Cite
Text
Zhang. "Hierarchical Latent Class Models for Cluster Analysis." AAAI Conference on Artificial Intelligence, 2002. doi:10.5555/777092.777131Markdown
[Zhang. "Hierarchical Latent Class Models for Cluster Analysis." AAAI Conference on Artificial Intelligence, 2002.](https://mlanthology.org/aaai/2002/zhang2002aaai-hierarchical/) doi:10.5555/777092.777131BibTeX
@inproceedings{zhang2002aaai-hierarchical,
title = {{Hierarchical Latent Class Models for Cluster Analysis}},
author = {Zhang, Nevin Lianwen},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2002},
pages = {230-237},
doi = {10.5555/777092.777131},
url = {https://mlanthology.org/aaai/2002/zhang2002aaai-hierarchical/}
}