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Finite-dimensional approximation of Gaussian processes

Author(s):

G. Ferrari-Trecate, C.K.I. Williams, M. Opper
Conference/Journal:

Advances in Neural Information Processing Systems 11, In M. Kearns, S. Solla and D. Cohn, editors.
Abstract:

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.

Year:

1999
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@Article { FerWil:1999:IFA_1400,
    author={G. Ferrari-Trecate and C.K.I. Williams and M. Opper},
    title={{Finite-dimensional approximation of Gaussian processes}},
    journal={Advances in Neural Information Processing Systems 11},
    year={1999},
    volume={},
    number={},
    pages={},
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=1400}
}
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