在米特空间中多重学习
1Program in Applied and Computational Mathematics, Princeton University, USA.
概括
这项研究介绍了在度量空间中多元学习的概括框架,超越了欧几里德距离. 它研究了图形拉普拉斯收的条件,并使用了像瓦瑟斯坦距离这样的替代指标.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 拓学的拓学
背景情况:
- 基于拉普拉斯的方法被广泛用于Euclidean空间 (RN) 中数据的维度减少.
- 这些方法的理论保证往往依赖于欧几里德距离对数据子多元体的地测距离的近似.
- 对于某些数据集,其他距离指标,如瓦瑟斯坦距离,可能比欧几里德距离更适合.
研究的目的:
- 将多元学习推广到任意的度量空间.
- 在使用非欧几里德度量表时,建立图形拉普拉斯的收的理论条件.
- 探索尺寸缩小中超越欧几里德距离的指标的适用性.
主要方法:
- 开发一个通用的理论框架,用于多元学习在度量空间.
- 对图形拉普拉斯运算符的点向收的分析.
- 对收保证所需的度量属性的调查.
主要成果:
- 提出了一个框架,将多元学习扩展到一般的度量空间.
- 在这些概括的设置中,已经确定了拉普拉斯图的点向收的足够条件.
- 这项研究证明了使用像瓦瑟斯坦距离这样的指标来减少维度的理论可能性.
结论:
- 拟议的框架扩大了基于拉普拉斯的尺寸缩小技术的适用性.
- 这些发现为在多元学习中使用各种距离指标提供了理论依据.
- 这项研究为复杂的数据结构应用先进的维度缩小方法开辟了道路,在复杂的数据结构中,欧几里德距离是次优的.
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