超越欧几里得:现代机器学习的插图指南,采用几何,拓和代数结构
Mathilde Papillon1,2, Sophia Sanborn3,2, Johan Mathe4,2
1UC Santa Barbara, Santa Barbara, United States of America.
概括
经典机器学习依赖于欧几里德几何,但现代人工智能越来越多地使用复杂的非欧几里德数据. 本综述探讨了在先进机器学习中分析几何,拓和代数结构的新方法.
科学领域:
- 机器学习 机器学习
- 几何几何学的几何学
- 数据科学数据科学数据科学
背景情况:
- 经典机器学习 (ML) 植根于欧几里德几何学,将其应用限制在欧几里德空间内的数据上.
- 现代机器学习将日益复杂的数据集与固有的非欧几里德结构 (几何,拓,代数) 相对应.
- 例子包括时空曲率,神经网络相互作用,以及物理学中的对称性转换.
研究的目的:
- 为非欧几里德式ML的新兴领域提供一个可访问的概述.
- 将几何,拓和代数ML的最新进展统一成一个图形分类.
- 在这个领域确定当前的挑战和未来的机会.
主要方法:
- 审查和综合用于机器学习的非欧几里德几何学的最新研究.
- 开发图形分类法来分类和整合各种非欧几里德式ML方法.
- 分析非欧几里德结构对ML算法的影响.
主要成果:
- 经典的ML方法正在被通用化,以处理复杂的非欧几里德数据.
- 提出了一个统一的框架来导航非欧几里德式ML的景观.
- 确定了关键的挑战和有前途的未来研究方向.
结论:
- 将ML调整为非欧几里德结构对于从复杂数据中提取知识至关重要.
- 拟议的分类学为理解这个快速发展的领域提供了一个结构化的方法.
- 通过几何学,拓学和代数学的视角来推进ML存在显著的机会.
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