Sparse Multi Gaussian Process Methods:
Multi-way Classification and beyond

Matthias Seeger

Abstract

While supervised kernel techniques involving a single latent function (or discriminant) are well understood and powerful methods have emerged, much less is known about models which combine a number of latent functions. We describe a generic way of generalizing the sparse Bayesian Gaussian process Informative Vector Machine (IVM) to such multi process models, emphasizing the key techniques which are required for an efficient solution (exploiting matrix structure, numerical quadrature). We apply our method to the multi-way classification problem, obtaining a scheme which scales essentially linearly in the number of datapoints and classes. We show how kernel parameters can be learned by empirical Bayesian techniques. We argue that a good solution for the multi-class problem leads to schemes for larger structured graphical models such as conditional random fields. Joint work with Michael Jordan.

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