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

• Published in: Journal of classification Vol. 38; no. 3; pp. 626 - 649
• Main Authors: Casa, Alessandro, Bouveyron, Charles, Erosheva, Elena, Menardi, Giovanna
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2021; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Bioinformatics; Clustering; Heterogeneity; Invariants; Marketing; Mathematics; Mathematics and Statistics; Multivariate analysis; Pattern Recognition; Psychometrics; Signal,Image and Speech Processing; Statistical Theory and Methods; Statistics; Time dependence; Stochastic EM; Latent block model; Co-clustering; Curve registration; co-clustering; stochastic EM; curve registration; latent block model
• ISSN: 0176-4268; 1432-1343; 1432-1343
• DOI: 10.1007/s00357-021-09402-8

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.