May 7th, 2013
Guillaume Bouchard , Machine Learning for Services (MLS) research group, will give a seminar: Convex methods for multi-view learning, link prediction and collective matrix factorization
"Many applications involve multiple interlinked data sources, but existing approach to handle them are often based on latent factor models which are difficult to learn. At the same time, recent advances in convex analysis, mainly based on the nuclear norm (relaxation of the matrix rank) and sparse structured approximations, have shown great theoretical and practical performances to handle very large matrix factorization problems with non-Gaussian noise and missing data. In this talk, we will show how multiple matrices can be jointly factorized using a convex formulation of the problem."