对于高维矩阵估值数据的最佳变量聚类
Inbeom Lee1, Siyi Deng2, Yang Ning3
1Booth School of Business, University of Chicago, 5807 S. Woodlawn Ave., Chicago, IL 60637, USA.
概括
本研究引入了一种新的潜在变量模型和对矩阵值数据的层次聚类算法,利用特征依赖结构. 拟议的方法实现了高维集群一致性和最佳性能,优于现有技术.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 矩阵值数据在各种应用中越来越常见.
- 现有的集群方法往往侧重于平均模型,忽视信息特征依赖结构.
- 这种限制在高维设置或平均值信息不足时尤为重要.
研究的目的:
- 为矩阵值数据开发一个新的潜在变量模型,该模型利用特征依赖结构进行聚类.
- 提出基于加权协差矩阵不相似度衡的等级聚类算法.
- 从理论上分析拟议方法的集群一致性和最佳性.
主要方法:
- 为矩阵值数据提出了一个新的潜在变量模型,该模型包含行和列成员矩阵.
- 一类等级聚类算法是使用加权协差矩阵的差异作为不相似度衡量标准开发的.
- 理论分析包括在高维设置中建立集群一致性,并导出最小值下限.
主要成果:
- 拟议的算法在高维设置中的温和条件下证明了集群一致性.
- 确定了协差矩阵的最佳权重,确保了最小速率-最佳性.
- 与现有方法相比,模拟研究显示出更高的性能,由更高的调整兰德指数 (ARI) 证明.
结论:
- 开发的潜在变量模型和层次聚类算法有效地利用了矩阵值数据的依赖结构.
- 该方法为高维集群提供理论保证,并实现最佳性能.
- 这种方法提供了实际的优势,并产生了有意义的解释,正如基因组数据集所示.
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