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.
翻译:我们开发了一个新的框架来加速高斯进程回归(GPR ) 。 特别是,我们考虑每个数据点的本地化内核会降低其他远处数据点的贡献,我们从应用这种本地化操作中得出GPR模式。 通过一系列实验,我们展示了拟议方法与全GPR、其他本地化模型和深高斯进程相比的竞争性表现。 至关重要的是,由于本地化操作引发的Gram矩阵的宽度效应,这些表现与标准的全球GPR相比,速度相当快。