Sparse Greedy Gaussian Process Regression

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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Alex Smola, Peter Bartlett


We present a simple sparse greedy technique to approximate the maximum a posteriori estimate of Gaussian Processes with much improved scaling behaviour in the sample size m. In particular, computational requirements are O(n2m), storage is O(nm), the cost for prediction is 0 ( n) and the cost to compute confidence bounds is O(nm), where n «: m. We show how to compute a stopping criterion, give bounds on the approximation error, and show applications to large scale problems.