Material Detail

Multi-Way, Multi-View Learning

Multi-Way, Multi-View Learning

This video was recorded at NIPS Workshops, Whistler 2009. 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 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.


  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material


Log in to participate in the discussions or sign up if you are not already a MERLOT member.