In this work, we propose a novel framework for large-scale Gaussian process (GP) modeling. Contrary to the global, and local approximations proposed in the literature to address the computational bottleneck with exact GP modeling, we employ a combined global-local approach in building the approximation. Our framework uses a subset-of-data approach where the subset is a union of a set of global points designed to capture the global trend in the data, and a set of local points specific to a given testing location to capture the local trend around the testing location. The correlation function is also modeled as a combination of a global, and a local kernel. The performance of our framework, which we refer to as TwinGP, is on par or better than the state-of-the-art GP modeling methods at a fraction of their computational cost.

翻译：本文提出一种用于大规模高斯过程建模的新型框架。与文献中为克服精确高斯过程计算瓶颈而提出的全局近似或局部近似不同，我们采用全局-局部联合方法构建近似。该框架采用数据子集策略，该子集由两部分构成：用于捕捉数据全局趋势的全局点集，以及针对特定测试位置用以捕捉其局部趋势的局部点集。核函数同样被建模为全局核与局部核的组合。我们提出的框架名为TwinGP，其性能与现有最优高斯过程建模方法持平或更优，而计算成本仅为其几分之一。