基于形状不变模型的时变数据联合聚类
Co-clustering of time-dependent data via Shape Invariant Model
摘要：
多变量时变数据，其中多个特征随时间被观察到的一组个人，在许多应用领域中越来越普遍。为了对这些数据进行建模，我们需要考虑时间和变量之间的关系，同时还要考虑受试者的异质性。我们提出了一种新的共同聚类方法，用于聚类的个人和变量，同时旨在处理功能和纵向数据。我们的方法借用了一些概念，从曲线配准框架中嵌入形状不变模型的潜在块模型，估计通过一个适当的修改SEM-Gibbs算法。由此产生的程序允许几个用户定义的规格的概念集群，可以选择实质性的理由，并提供了简约的总结复杂的纵向或功能数据的数据矩阵划分成同质块。
英文摘要：
Multivariate time-dependent data, where multiple features are observed over time for a set of individuals, are increasingly widespread in many application domains. To model these data we need to account for relations among both time instants and variables and, at the same time, for subjects heterogeneity. We propose a new co-clustering methodology for clustering individuals and variables simultaneously that is designed to handle both functional and longitudinal data. Our approach borrows some concepts from the curve registration framework by embedding the Shape Invariant Model in the Latent Block Model, estimated via a suitable modification of the SEM-Gibbs algorithm. The resulting procedure allows for several user-defined specifications of the notion of cluster that could be chosen on substantive grounds and provides parsimonious summaries of complex longitudinal or functional data by partitioning data matrices into homogeneous blocks.
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