Finite-Dimensional Approximation of Gaussian Processes
Abstract
Gaussian process (GP) prediction suffers from O(n3) scaling with the data set size n. By using a finite-dimensional basis to approximate the GP predictor, the computational complexity can be reduced. We de(cid:173) rive optimal finite-dimensional predictors under a number of assump(cid:173) tions, and show the superiority of these predictors over the Projected Bayes Regression method (which is asymptotically optimal). We also show how to calculate the minimal model size for a given n. The calculations are backed up by numerical experiments.
Cite
Text
Ferrari-Trecate et al. "Finite-Dimensional Approximation of Gaussian Processes." Neural Information Processing Systems, 1998.Markdown
[Ferrari-Trecate et al. "Finite-Dimensional Approximation of Gaussian Processes." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/ferraritrecate1998neurips-finitedimensional/)BibTeX
@inproceedings{ferraritrecate1998neurips-finitedimensional,
title = {{Finite-Dimensional Approximation of Gaussian Processes}},
author = {Ferrari-Trecate, Giancarlo and Williams, Christopher K. I. and Opper, Manfred},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {218-224},
url = {https://mlanthology.org/neurips/1998/ferraritrecate1998neurips-finitedimensional/}
}