Clustering for Bivariate Functional Data  

作　　者：Shi-yun CAO Yan-qiu ZHOU Yan-ling WAN Tao ZHANG 

机构地区：[1]School of Science,Guangxi University of Science and Technology,Liuzhou 545006,China [2]School of Marxism,Guangxi University of Science and Technology,Liuzhou 545006,China [3]Key Laboratory of Intelligent Information Processing and Graph Processing,Guangxi University of Science and Technology,Liuzhou 545006,China

出　　处：《Acta Mathematicae Applicatae Sinica》2024年第3期613-629,共17页应用数学学报（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos.12261007);Natural Science Foundation of Guangxi Province (Grant No.2020GXNSFAA297225)。

摘　　要：In this paper,we consider the clustering of bivariate functional data where each random surface consists of a set of curves recorded repeatedly for each subject.The k-centres surface clustering method based on marginal functional principal component analysis is proposed for the bivariate functional data,and a novel clustering criterion is presented where both the random surface and its partial derivative function in two directions are considered.In addition,we also consider two other clustering methods,k-centres surface clustering methods based on product functional principal component analysis or double functional principal component analysis.Simulation results indicate that the proposed methods have a nice performance in terms of both the correct classification rate and the adjusted rand index.The approaches are further illustrated through empirical analysis of human mortality data.

关 键 词：bivariate functional data -centres surface clustering functional principal component analysis partial derivative function 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]