  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.
