Fast Forward Selection to Speed Up Sparse Gaussian Process Regression


We present a method for the sparse greedy approximation of Bayesian Gaussian process regression, featuring a novel heuristic for very fast forward selection. Our method is essentially as fast as an equivalent one which selects the " support " patterns at random, yet it can outperform random selection on hard curve fitting tasks. More importantly, it leads to a sufficiently stable approximation of the log marginal likelihood of the training data, which can be optimised to adjust a large number of hyperparameters automatically. We demonstrate the model selection capabilities of the algorithm in a range of experiments. In line with the development of our method, we present a simple view on sparse approximations for GP models and their underlying assumptions and show relations to other methods.

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Observations on the Nyström method for Gaussian process prediction

  • Christopher K I Williams, C E Rasmussen, A Schwaighofer, V Tresp
  • 2002
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