English(EN) Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes

新的高斯过程核模型可处理空间数据中的旋转各向异性

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-11 19:42

研究人员开发了一种新的可解释高斯过程核，能够模拟3D空间场中的旋转各向异性。该核显式参数化了主长度尺度和方向，比标准的轴对齐方法或通用的SPD度量提供了更直观的方法。该方法在合成数据和材料密度数据集上进行了测试，显示出改进的预测性能，并能够揭示现有技术无法捕捉的复杂各向异性。 AI

影响引入了一种更具可解释性的复杂空间数据建模方法，有望改善需要精确方向分析的领域的应用。

排序理由该集群包含一篇详细介绍新机器学习方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Kane Warrior, Dalia Chakrabarty · 2026-05-13 04:00

Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes

arXiv:2605.11179v1 Announce Type: new Abstract: Many three-dimensional spatial fields are anisotropic, with directions of rapid and slow variation that need not align with the coordinate axes. Standard Gaussian process kernels with Automatic Relevance Determination (ARD) capture …
arXiv stat.ML TIER_1 English(EN) · Dalia Chakrabarty · 2026-05-11 19:42

Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes

Many three-dimensional spatial fields are anisotropic, with directions of rapid and slow variation that need not align with the coordinate axes. Standard Gaussian process kernels with Automatic Relevance Determination (ARD) capture only axis-aligned anisotropy, while generic full…