Finite-dimensional approximation of Gaussian processes

Gaussian process (GP) prediction suffers from O(n^3) 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 derive optimal finite-dimensional predictors under a number of assumptions, 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.


Editor(s):
Cohn, M. Kearns AND S. Solla AND D.
Published in:
Advances in Neural Information Processing Systems, 11, 218-224
Year:
1999
Publisher:
MIT Press
ISBN:
0-262-11245-0
Laboratories:




 Record created 2017-01-10, last modified 2018-03-17


Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)