Covariance Kernels from Bayesian Generative Models
Matthias Seeger · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2001
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task data using Bayesian techniques. We describe an implementation of this framework which uses variational Bayesian mixtures of factor analyzers in order to attack classification problems in high-dimensional spaces where labeled data is sparse, but unlabeled data is abundant. 1