利用衍生主成分分析对功能数据进行基于距离的聚类

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-06-11 DOI:10.1080/10618600.2024.2366499

Ping Yu, Gongmin Shi, Chunjie Wang, Xinyuan Song

引用次数: 0

摘要

函数数据分析（FDA）是处理无限维数据的重要现代范式。功能数据分析的一项重要任务是聚类，即根据测量值的形状来确定子组。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Distance-based clustering of functional data with derivative principal component analysis

Functional data analysis (FDA) is an important modern paradigm for handling infinite-dimensional data. An important task in FDA is clustering, which identifies subgroups based on the shapes of meas...

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.