瓶颈
计算机科学
核(代数)
高斯过程
比例(比率)
过程(计算)
数学优化
集合(抽象数据类型)
高斯分布
分数(化学)
功能(生物学)
算法
数学
地理
操作系统
组合数学
物理
生物
进化生物学
嵌入式系统
量子力学
地图学
有机化学
化学
程序设计语言
作者
Akhil Vakayil,Roshan Joseph
出处
期刊:Cornell University - arXiv
日期:2023-05-17
标识
DOI:10.1080/00401706.2023.2296451
摘要
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.
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