Scaling Gaussian Process Regression for Big Data

Gaussian process (GP) regression are non-parametric supervised learning methods in the field of machine learning. GP methods has excellent prediction performance, but need too much time on training models, because it has to solve a square matrix whose number of rows and columns are linear to the number of training data points, resulting in cubed time complexity. We proposed a method that uses clustering algorithm to speed up the training phase and approximate the prediction. The experiments show that our method costs less than one seventieth time of original GP given the training set has forty thousand data points while the error does not grow much. Compared to other approximation methods, our method uses less time and obtain prediction of less error.