Copula Mixture Model for Dependency-seeking Clustering

TL;DR

This paper presents a copula mixture model for dependency-seeking clustering that enhances flexibility and interpretability in multivariate data analysis using a non-parametric Bayesian approach.

Contribution

It introduces a novel copula-based mixture model extending canonical correlation analysis for better dependency-seeking clustering with arbitrary continuous marginals.

Findings

Improved clustering accuracy on synthetic and real datasets.

Enhanced interpretability of clustering results.

Demonstrated flexibility over traditional models.

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.