关于柏拉图洞穴中的多重学习:关于多重学习和物理现象的评论
Roy R Lederman1, Bogdan Toader1
1Department of Statistics and Data Science, Yale University, New Haven, Connecticut, USA.
概括
机器学习经常从测量中推断出底层物理现象的几何学. 这项研究强调了测量几何学差异如何可以导致错误的结论在多边学习和缩小维度.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 应用数学 应用数学 应用数学
背景情况:
- 许多机器学习技术从数据中推断出低维的多元结构.
- 这些方法往往缺乏物理现象或测量装置的明确模型.
- 了解测量几何和现象几何之间的关系至关重要.
研究的目的:
- 介绍一下多元学习中的度量变形的一个警告示例.
- 为了说明数据处理如何引入测量和现象几何之间的差异.
- 展示缩小维度和无监督学习中的潜在陷.
主要方法:
- 在测量空间中测量变形的数学分析.
- 使用标准数据处理程序的说明性示例.
- 专注于多种学习技巧.
主要成果:
- 确定了一个简单而可概括的度量变形效应.
- 由于这种效应,一个良性的数据处理步骤被证明产生了不正确的结果.
- 测量和底层现象几何之间的差异是非常重要的.
结论:
- 测量的几何形状可能与底层现象的几何形状有很大不同.
- 标准数据处理可以无意中在多元学习中引入错误.
- 这些问题与缩小维度和无监督学习有广泛的相关性.
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