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Copula mixture model for eependency-seeking clustering

Melanie, Rey and Roth, Volker. (2012) Copula mixture model for eependency-seeking clustering. In: 29th International Conference on Machine Learning (ICML 2012), 8 S.. Edinburgh.

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Official URL: http://edoc.unibas.ch/dok/A6018449

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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.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenanalyse (Roth)
UniBasel Contributors:Roth, Volker and Rey, Mélanie
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Bibsysno:Link to catalogue
Publisher:International Machine Learning Society
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:13 Sep 2013 08:00
Deposited On:13 Sep 2013 07:58

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