Sparse Representation for Gaussian Process Models

We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a combination of a Bayesian online al(cid: 173) gorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the model. Experi(cid: 173) mental results on toy examples and large real-world data sets indicate the efficiency of the approach.

Authors

• Lehel Csató
• Manfred Opper

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