A Multivariate Unimodality Test Harnenssing the Dip Statistic of Mahalanobis Distances Over Random Projections.
CoRR(2023)
摘要
Unimodality, pivotal in statistical analysis, offers insights into dataset
structures and drives sophisticated analytical procedures. While unimodality's
confirmation is straightforward for one-dimensional data using methods like
Silverman's approach and Hartigans' dip statistic, its generalization to higher
dimensions remains challenging. By extrapolating one-dimensional unimodality
principles to multi-dimensional spaces through linear random projections and
leveraging point-to-point distancing, our method, rooted in
$\alpha$-unimodality assumptions, presents a novel multivariate unimodality
test named mud-pod. Both theoretical and empirical studies confirm the efficacy
of our method in unimodality assessment of multidimensional datasets as well as
in estimating the number of clusters.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要