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book part or chapter
Finite-dimensional approximation of Gaussian processes
Opper, M.
•
Ferrari-Trecate, G.
•
Williams, C. K. I
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.
Type
book part or chapter
Authors
Opper, M.
•
Ferrari-Trecate, G.
•
Williams, C. K. I
Editors
Cohn, D.
•
Kearns, M.
•
Solla, S.
Publication date
1999
Publisher
Published in
Advances in Neural Information Processing Systems
ISBN of the book
0-262-11245-0
Start page
218
End page
224
Volume
11
Peer reviewed
REVIEWED
EPFL units
Available on Infoscience
January 10, 2017
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