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Splitting Gaussian processes for computationally-efficient regression

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  • Nick Terry
  • Youngjun Choe

Abstract

Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations. In particular, the cubic time complexity of updating standard Gaussian process models can be a limiting factor in applications. We propose an algorithm for sequentially partitioning the input space and fitting a localized Gaussian process to each disjoint region. The algorithm is shown to have superior time and space complexity to existing methods, and its sequential nature allows the model to be updated efficiently. The algorithm constructs a model for which the time complexity of updating is tightly bounded above by a pre-specified parameter. To the best of our knowledge, the model is the first local Gaussian process regression model to achieve linear memory complexity. Theoretical continuity properties of the model are proven. We demonstrate the efficacy of the resulting model on several multi-dimensional regression tasks.

Suggested Citation

  • Nick Terry & Youngjun Choe, 2021. "Splitting Gaussian processes for computationally-efficient regression," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-17, August.
    • Handle: RePEc:plo:pone00:0256470
    DOI: 10.1371/journal.pone.0256470
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    References listed on IDEAS

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    1. Gramacy, Robert B & Lee, Herbert K. H, 2008. "Bayesian Treed Gaussian Process Models With an Application to Computer Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1119-1130.
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