Sparse Spectral Sampling Gaussian Processes
In this work we introduce Power Spectral Density Sampling (PSDS), a new method for building a reduced rank approximation to a stationary kernel matrix. We apply this method in the framework of Gaussian Processes (GP). The resulting model can be trained in an online fashion: As each training sample arrives, we can include it in the model using only O(r2) time and storage, where r is the selected number of spectral samples used in the approximation. For n training points, the overall cost is O(nr2) time and O(r2) storage. The resulting GP is properly defined and model hyper-parameters can be selected by evidence maximization. Though it is especially well-suited for low dimensional problems, it can reach and even outperform other (batch) state-of-the-art sparse GP methods on higher dimensional datasets if allowed to work in batch mode, learning the locations of the spectral samples. We check this possibility on some large datasets.