A Nonparametric Variable Clustering Model
Abstract
Factor analysis models effectively summarise the covariance structure of high dimensional data, but the solutions are typically hard to interpret. This motivates attempting to find a disjoint partition, i.e. a clustering, of observed variables so that variables in a cluster are highly correlated. We introduce a Bayesian non-parametric approach to this problem, and demonstrate advantages over heuristic methods proposed to date.
Cite
Text
Palla et al. "A Nonparametric Variable Clustering Model." Neural Information Processing Systems, 2012.Markdown
[Palla et al. "A Nonparametric Variable Clustering Model." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/palla2012neurips-nonparametric/)BibTeX
@inproceedings{palla2012neurips-nonparametric,
title = {{A Nonparametric Variable Clustering Model}},
author = {Palla, Konstantina and Ghahramani, Zoubin and Knowles, David A.},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {2987-2995},
url = {https://mlanthology.org/neurips/2012/palla2012neurips-nonparametric/}
}