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 algorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the model. Experimental results on toy examples and large real-world datasets indicate the efficiency of the approach.

• Type: Conference
• Year: 2000
• Where: NIPS
• Authors: Lehel Csató, Manfred Opper
• Keywords: Large Data Sets, Large Real-world Datasets, NIPS 2000, NIPS 2007, Sparse Representation