Model-based clustering for multivariate functional data
Model-based clustering for multivariate functional data
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DOI：
10.1016/j.csda.2012.12.004
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发表时间：
2014-03-01
影响因子：
1.8
通讯作者：
Preda, Cristian
中科院分区：
文献类型：
--
作者：
Jacques, Julien;Preda, Cristian
提出了第一个基于模型的多元函数数据聚类算法。在引入多元函数主成分分析（MFPCA）的基础上，基于主成分分数正态性假设，定义了一个参数混合模型，并利用EM类算法进行了估计.该模型的主要优点是它能够考虑曲线之间的依赖性。模拟和真实的数据集上的实验结果表明了该方法的有效性。(C)2012 Elsevier B.V.保留所有权利。
The first model-based clustering algorithm for multivariate functional data is proposed. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, based on the assumption of normality of the principal component scores, is defined and estimated by an EM-like algorithm. The main advantage of the proposed model is its ability to take into account the dependence among curves. Results on simulated and real datasets show the efficiency of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.