摘要：AbstractWe provide a method which allows for online updating of sparse Gaussian Process (GP) regression algorithms for any set of inducing inputs. This method is derived both for the Fully Independent Training Conditional (FITC) and the Partially Independent Training Conditional (PITC) approximation, and it allows the inclusion of a new measurement point xn+1 in O(m2) time, with m denoting the size of the set of inducing inputs. Due to the online nature of the algorithms, it is possible to forget earlier measurement data, which means that also the memory space required is O(m2), both for FITC and PITC. We show that this method is able to efficiently apply GP regression to a large data set with accurate results.
