Sparse on-line Gaussian processes

Lehel Csató, Manfred Opper

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Pages (from-to)641-668
    Number of pages28
    JournalNeural Computation
    Issue number3
    Publication statusPublished - Mar 2002


    • sparse representations
    • Gaussian Process
    • large data sets
    • online algorithm


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