Sparse Greedy Gaussian Process Regression
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 O(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.