Co-clustering of Time-Dependent Data via the Shape Invariant Model

被引:0
|
作者
Alessandro Casa
Charles Bouveyron
Elena Erosheva
Giovanna Menardi
机构
[1] University College Dublin,School of Mathematics & Statistics, Vistamilk SFI Research Centre
[2] Université Côte d’Azur,INRIA, CNRS, Laboratoire J.A. Dieudonné, MAASAI research team
[3] University of Washington,Department of Statistics
[4] University of Padova,Deparment of Statistical Sciences
来源
Journal of Classification | 2021年 / 38卷
关键词
Co-clustering; Curve registration; Latent block model; Stochastic EM;
D O I
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中图分类号
学科分类号
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 subject heterogeneity. We propose a new co-clustering methodology for grouping individuals and variables simultaneously, 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 can be chosen on substantive grounds and provides parsimonious summaries of complex time-dependent data by partitioning data matrices into homogeneous blocks. Along with the explicit modelling of time evolution, these aspects allow for an easy interpretation of the clusters, from which also low-dimensional settings may benefit.
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页码:626 / 649
页数:23
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