Copula Mixture Model for Dependency-seeking Clustering

2012/06/27 by Mélanie Rey, Rey, Melanie, Volker Röth +1 · 2 citations
Computer Science · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.1206.6433

openalex publication_date 2012/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Bridged Clustering: Semi-Supervised Sparse Bridging
• Deep Copula Classifier: Theory, Consistency, and Empirical Evaluation

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