Circular Pseudo-Point Approximations for Scaling Gaussian Processes

We introduce a new approach for performing accurate and computationally efficient posterior inference for Gaussian Process regression problems that exploits the combination of pseudo-point approximations and approximately circulant covariance structure. We argue mathematically that the new technique has substantially lower asymptotic complexity than traditional pseudo-point approximations and demonstrate empirically that it returns results that are very close to those obtained using exact inference.