Two-step Gaussian Process Regression Improving Performance of Training and Prediction

Abstract

Since Gaussian process regression (GPR) cannot feasibly be applied to big and growing data sets, this paper introduces an integration algorithm called Two-step Gaussian Process Regression (TGPR) which speeds up both training and prediction to solve the problem. First, analyze the basics behind regular GPR. Then, introduce TGPR by using the inducing inputs to optimize the regular GPR algorithm. Last, apply TGPR to a three-dimension model, the experimental results compared with regular GPR show that TGPR is faster and more accurate. Keywords—gaussian process; regression; inducing inputs; hyperparameter