在数据中发现图形结构和功能关系的编码:连接点的高斯过程框架
Théo Bourdais1, Pau Batlle1, Xianjin Yang1
1Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125.
概括
本研究介绍了高斯过程框架,用于从数据中发现复杂的超图结构. 这种方法有效地接近未知的函数和结构,推进计算知识处理.
科学领域:
- 计算数学是指计算数学.
- 机器学习是机器学习.
- 图形理论是指图形的理论.
背景情况:
- 科学问题往往涉及三个复杂度级别的函数近似.
- 超图为组织计算知识提供了一个框架.
- 第三类问题需要同时发现超图结构和近似函数.
研究的目的:
- 为3型问题引入可解释的高斯过程 (GP) 框架.
- 为了应对从部分观测中发现未知的超图结构的挑战.
- 为现有的因果推理方法提供一种有效的替代方案.
主要方法:
- 使用高斯过程 (GPs) 作为传感机制.
- 利用全科医生的非线性ANOVA能力.
- 开发一个具有多项式复杂性的框架.
主要成果:
- 拟议的GP框架使得数据驱动的超图结构发现成为可能.
- 它有效地近似未知的变量和函数,没有数据随机化或稀疏性假设.
- 实现多项式复杂性,超过因果推理方法的超指数复杂性.
结论:
- 可解释的GP框架为复杂的计算知识发现提供了一个强大的方法.
- 这种方法提升了模拟和分析具有未知底层结构的系统的能力.
- 它为涉及超图结构学习的问题提供了计算效率高的解决方案.
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