Sparse on-line Gaussian processes


We develop an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian online algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the GP model. By using an appealing parametrisation and projection techniques that use the RKHS norm, recursions for the effective parameters and a sparse Gaussian approximation of the posterior process are obtained. This allows both for a propagation of predictions as well as of Bayesian error measures. The significance and robustness of our approach is demonstrated on a variety of experiments.

Divisions: Aston University (General)
Uncontrolled Keywords: sparse representations,Gaussian Process,large data sets,online algorithm
PURE Output Type: Technical report
Published Date: 2002-09-09
Authors: Csató, Lehel
Opper, Manfred



Version: Published Version

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