Multi-Way, Multi-View Learning
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.
Keywords
Cite
@article{arxiv.0912.3211,
title = {Multi-Way, Multi-View Learning},
author = {Ilkka Huopaniemi and Tommi Suvitaival and Janne Nikkilä and Matej Orešič and Samuel Kaski},
journal= {arXiv preprint arXiv:0912.3211},
year = {2009}
}
Comments
9 pages, 4 figures