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arXiv · 0912.3211

Multi-Way, Multi-View Learning

Ilkka Huopaniemi·Tommi Suvitaival·Janne Nikkilä·Matej Orešič·Samuel Kaski
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Abstract

We extend multi-way, multivariate ANOVA-type analysis to cases where one covariate is the view, with features of each view coming from different, high-dimensional domains. The different views are assumed to be connected by having paired samples; this is a common setup in recent bioinformatics experiments, of which we analyze metabolite profiles in different conditions (disease vs. control and treatment vs. untreated) in different tissues (views). We introduce a multi-way latent variable model for this new task, by extending the generative model of Bayesian canonical correlation analysis (CCA) both to take multi-way covariate information into account as population priors, and by reducing the dimensionality by an integrated factor analysis that assumes the metabolites to come in correlated groups.

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Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Orešič, Samuel Kaski. 2009-12-16. Multi-Way, Multi-View Learning. https://arxiv.org/abs/0912.3211

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