核回归的退化与母核进入低阶多项式回归在高维度
Sergei Manzhos1, Manabu Ihara1
1School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan.
The Journal of chemical physics
|January 8, 2024
概括
像高斯过程这样的内核方法可以在高维中失去其优势,有效地成为多项式回归. 这凸显了材料信息学和潜在能源表面配件中对物理动机的核心的需求.
科学领域:
- 计算化学是一种计算化学.
- 材料信息学 材料信息学
- 机器学习 机器学习
背景情况:
- 内核方法,包括内核回归和高斯过程回归与Matern类型内核,被广泛应用.
- 这些方法对于装配潜在能量表面 (PES) 和密度函数以及材料信息学至关重要.
- 高维特征空间通常需要使用这些内核方法的稀疏数据.
研究的目的:
- 为了研究Matern类型内核在高维特征空间中与稀疏数据的行为.
- 确定核心方法是否以及何时退化为低阶多项式回归.
- 提供关于多项式近似的有效性和内核设计的重要性的见解.
主要方法:
- 在高维,稀疏的数据模式中对Matern类型内核的理论分析.
- 使用六维和十五维分子PES的数值演示.
- 与平方指数和简单指数核心进行比较.
主要成果:
- 马特恩型内核的最佳长度参数在高维度中可能变得过大.
- 核心方法可以有效地退化为低阶多项式回归,失去其独特的优势.
- 通过分子PES示例证明了这种退化.
结论:
- 对于中等大小的分子,像PIP这样的多项式近似的成功部分是由这个现象解释的.
- 物理动机或繁殖的核对于保持Matern类型核的好处很重要.
- 精心选择内核对于有效应用高维问题中的内核方法至关重要.
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