Variational Bayesian Mixture of Robust CCA Models
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.
Cite
Text
Viinikanoja et al. "Variational Bayesian Mixture of Robust CCA Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15939-8_24Markdown
[Viinikanoja et al. "Variational Bayesian Mixture of Robust CCA Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/viinikanoja2010ecmlpkdd-variational/) doi:10.1007/978-3-642-15939-8_24BibTeX
@inproceedings{viinikanoja2010ecmlpkdd-variational,
title = {{Variational Bayesian Mixture of Robust CCA Models}},
author = {Viinikanoja, Jaakko and Klami, Arto and Kaski, Samuel},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {370-385},
doi = {10.1007/978-3-642-15939-8_24},
url = {https://mlanthology.org/ecmlpkdd/2010/viinikanoja2010ecmlpkdd-variational/}
}