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We introduce a Dirichlet prior mixture of meta-Gaussian distributions to perform dependency-seeking clustering when co-occurring samples from different data sources are available. The model extends Bayesian mixtures of Canonical Correlation Analysis clustering methods to multivariate data distributed with arbitrary continuous margins. Using meta-Gaussian distributions gives the freedom to specify each margin separately and thereby also enables clustering in the joint space when the data are differently distributed in the different views. The Bayesian mixture formulation retains the advantages of using a Dirichlet prior. We do not need to specify the number of clusters and the model is less prone to overfitting than non-Bayesian alternatives. Inference is carried out using a Markov chain sampling method for Dirichlet process mixture models with non-conjugate prior adapted to the copula mixture model. Results on different simulated data sets show significant improvement compared to a Dirichlet prior Gaussian mixture and a mixture of CCA model.

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