# Sparse Gaussian Processes using Pseudo-inputs

@inproceedings{Snelson2005SparseGP, title={Sparse Gaussian Processes using Pseudo-inputs}, author={Edward Snelson and Zoubin Ghahramani}, booktitle={NIPS}, year={2005} }

We present a new Gaussian process (GP) regression model whose co-variance is parameterized by the the locations of M pseudo-input points, which we learn by a gradient based optimization. We take M ≪ N, where N is the number of real data points, and hence obtain a sparse regression method which has O(M2N) training cost and O(M2) prediction cost per test case. We also find hyperparameters of the covariance function in the same joint optimization. The method can be viewed as a Bayesian regression…

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