Fast Sparse Gaussian Process Methods: The Informative Vector Machine

by Neil Lawrence, Matthias Seeger and Ralf Herbrich

This NIPS paper doesn't cover much as the technical report does. They are exploring a sample selection methods for Gaussian processes. They have studied regression, classification and ordinal regression problems. The idea originates from ADF (looks like EP). It is a kind of greedy algorithm that selects the best d samples that maximizes a certain criterion---differential entropy score.

I am thinking about Nystrom approximation. The two methods have different intention (perhaps?): IVM aims at finding sparse representation (just like what SVM achieves via optimization, IVM is a little brute-force though); Nystrom method is a more general approximation method for all kernel methods. The former is supervised while the latter unsupervised.

But is IVM extensible for other kernel-based classifiers and regressors?