Locally Smoothed Gaussian Process Regression
Davit Gogolashvili, Bogdan L. Kozyrskiy, Maurizio Filippone
Abstract
We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. Through a set of experiments, we demonstrate the competitive performance of the proposed approach compared to full GPR, other localized models, and deep Gaussian processes. Crucially, these performances are obtained with considerable speedups compared to standard global GPR due to the sparsification effect of the Gram matrix induced by the localization operation.
Topics & Concepts
Ground-penetrating radarComputer scienceKrigingGaussian processRegressionProcess (computing)GaussianAlgorithmSet (abstract data type)Data setData miningPoint (geometry)Artificial intelligenceMathematical optimizationMachine learningStatisticsRadarMathematicsTelecommunicationsProgramming languageGeometryOperating systemQuantum mechanicsPhysicsGaussian Processes and Bayesian InferenceAdvanced Multi-Objective Optimization AlgorithmsMachine Learning and Algorithms