通过分半自向量匹配 (SHEM) 估计主要组件的数量
1Experience Design Team, Institute for Globally Distributed Open Research and Education (IGDORE), Sopra Steria, 6th Floor, 1 Bartholomew Close, EC1A 7BL London, United Kingdom.
MethodsX
|July 31, 2023
概括
这项研究引入了一种新方法,用于估计缩小尺寸的主要组件的数量. 通过比较数据分割,它提供了一种更准确的方法来确定独立组件的真实数量.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 主要组件分析 (PCA) 对于尺寸缩小至关重要.
- 现有的选择主要组件数量的方法可能是不理想的.
- 精确的组件选择对于可靠的数据分析至关重要.
研究的目的:
- 介绍一种用于估计主要组件最佳数量的替代程序.
- 为了恢复数据生成的独立向量的真实数量.
- 通过数据分割和相似性分析验证一种新方法.
主要方法:
- 重复将数据分成随机的半部分.
- 比较从每个数据中获得的自向量.
- 分析高和低相似度得分之间的分割,以估计组件数量.
主要成果:
- 提出的方法证明了估计主要组件的原则证明.
- 跨数据分割的相似性分析提供了一个有用的估计方法.
- 该技术适用于缩小尺寸和类似的建模问题.
结论:
- 数据分割和相似性比较为确定主要组件数量的可行策略提供了一个可行的策略.
- 这种方法提高了尺寸缩小技术的准确性.
- 这种方法有望改善各种统计建模应用程序.
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