Copula Mixture Model for Dependency-seeking Clustering
This work addresses clustering challenges in multivariate data analysis for researchers in statistics and machine learning, representing an incremental advancement.
The authors tackled the problem of dependency-seeking clustering for co-occurring samples from multiple data sources by introducing a copula mixture model, which significantly improved clustering and interpretability on synthetic and real data.
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.