Mean shift-based clustering for misaligned functional data

被引：0
|
作者：
Welbaum, Andrew [1 ]
Qiao, Wanli [1 ]
机构：
[1] George Mason Univ, Dept Stat, Fairfax, VA 22030 USA
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2025年 / 206卷
基金：
美国国家科学基金会;
关键词：
Clustering; Functional data; Mean shift; Gradient ascent; Misalignment; Elastic distance; CONVERGENCE; SEEKING;
D O I：
10.1016/j.csda.2024.108107
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Misalignment often occurs in functional data and can severely impact their clustering results. A clustering algorithm for misaligned functional data is developed, by adapting the original mean shift algorithm in the Euclidean space. This mean shift algorithm is applied to the quotient space of the orbits of the square root velocity functions induced by the misaligned functional data, in which the elastic distance is equipped. Convergence properties of this algorithm are studied. The efficacy of the algorithm is demonstrated through simulations and various real data applications.
引用
收藏
页数：26
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