Observations on the Nyström Method for Gaussian Process Prediction

Chris Williams, Institute for Adaptive and Neural Computation, Division of Informatics, University of Edinburgh
Carl Edward Rasmussen, Gatsby Computational Neuroscience Unit, UCL
Anton Schwaighofer, Institute for Theoretical Computer Science, Graz University of Technology
Volker Tresp, Department of Neural Computation, Siemens Corporate Technology.

A number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including the Nyström method of Williams and Seeger (2001). In this paper we focus on two issues (1) the relationship of the Nyström method to the Subset of Regressors method (Poggio and Girosi 1990; Luo and Wahba, 1997) and (2) understanding in what circumstances the Nyström approximation would be expected to provide a good approximation to exact GP regression.

Technical Report.

Available as ps.