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Locally induced Gaussian processes for large-scale simulation experiments

机译：用于大规模仿真实验的本地诱导高斯工艺

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摘要

Gaussian processes (GPs) serve as flexible surrogates for complex surfaces, but buckle under the cubic cost of matrix decompositions with big training data sizes. Geospatial and machine learning communities suggest pseudo-inputs, or inducing points, as one strategy to obtain an approximation easing that computational burden. However, we show how placement of inducing points and their multitude can be thwarted by pathologies, especially in large-scale dynamic response surface modeling tasks. As remedy, we suggest porting the inducing point idea, which is usually applied globally, over to a more local context where selection is both easier and faster. In this way, our proposed methodology hybridizes global inducing point and data subset-based local GP approximation. A cascade of strategies for planning the selection of local inducing points is provided, and comparisons are drawn to related methodology with emphasis on computer surrogate modeling applications. We show that local inducing points extend their global and data subset component parts on the accuracy-computational efficiency frontier. Illustrative examples are provided on benchmark data and a large-scale real-simulation satellite drag interpolation problem.
机译：高斯工艺（GPS）用作复杂表面的灵活代理，但是以大训练数据大小的矩阵分解的立方成本扣除。地理空间和机器学习社区建议伪输入或诱导点，作为一种获得计算负担的近似的一种策略。然而，我们展示了诱导点的放置以及众多的诱导点可以被病理突然挫败，特别是在大规模的动态响应面建模任务中。作为补救措施，我们建议移植诱导点的想法，该想法通常在全球范围内应用，而是在选择既越来越容易且更快的本地上下文。通过这种方式，我们提出的方法杂交了全局诱导点和基于数据子集的本地GP近似。提供了规划选择本地诱导点的级联策略，并绘制了相关方法，重点是计算机代理建模应用。我们表明本地诱导点在精度计算效率前沿扩展其全局和数据子集件部件。在基准数据和大规模实仿真卫星拖动插值问题上提供了说明性示例。

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