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Scaling Multidimensional Gaussian Processes using Projected Additive Approximations
Authors: Elad Gilboa, Yunus Saatçci and John P. Cunningham
Conference: Proceedings of the 30th International Conference on Machine Learning (ICML-13)
Abstract: Exact Gaussian Process (GP) regression has $O(N^3)$ runtime for data size N, making it intractable for large N. Advances in GP scaling have not been extended to the multidimensional input setting, despite the preponderance of multidimensional applications. This paper introduces and tests a novel method of projected additive approximation to multidimensional GPs. We thoroughly illustrate the power of this method on several datasets, achieving close performance to the naive Full GP at orders of magnitude less cost.
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