Variational Bayesian mixture of robust CCA models

Authors:
Jaakko Viinikanoja;Arto Klami;Samuel Kaski
Affiliations:
Aalto University, School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology;Aalto University, School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology;Aalto University, School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Year:
2010

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Abstract

We study the problem of extracting statistical dependencies between multivariate signals, to be used for exploratory analysis of complicated natural phenomena. In particular, we develop generative models for extracting the dependencies, made possible by the probabilistic interpretation of canonical correlation analysis (CCA). We introduce a mixture of robust canonical correlation analyzers, using t-distribution to make the model robust to outliers and variational Bayesian inference for learning from noisy data. We demonstrate the improvements of the new model on artificial data, and further apply it for analyzing dependencies between MEG and measurements of autonomic nervous system to illustrate potential use scenarios.