Covariance Kernels from Bayesian Generative Models

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

Authors

Matthias Seeger

Abstract

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 frame(cid:173) work 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.