Multi-Way, Multi-View Learning
نویسندگان
چکیده
We extend multi-way, multivariate ANOVA-type analysis to cases where one covariate is the view, with features of each view coming from different, highdimensional domains. The different views are assumed to be connected by having paired samples; this is common in our main application, biological experiments integrating data from different sources. Such experiments typically also include a controlled multi-way experimental setup where disease status, medical treatment groups, gender and time of the measurement are usual covariates. 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 features to come in correlated groups.
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