Copula Mixture Model for Dependency-seeking Clustering
Methodology
2012-07-03 v1 Machine Learning
Machine Learning
Abstract
We introduce a copula mixture model to perform dependency-seeking clustering when co-occurring samples from different data sources are available. The model takes advantage of the great flexibility offered by the copulas framework to extend mixtures of Canonical Correlation Analysis to multivariate data with arbitrary continuous marginal densities. We formulate our model as a non-parametric Bayesian mixture, while providing efficient MCMC inference. Experiments on synthetic and real data demonstrate that the increased flexibility of the copula mixture significantly improves the clustering and the interpretability of the results.
Cite
@article{arxiv.1206.6433,
title = {Copula Mixture Model for Dependency-seeking Clustering},
author = {Melanie Rey and Volker Roth},
journal= {arXiv preprint arXiv:1206.6433},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)