Mixture models for simultaneous classification and reduction of three-way data

Roberto Rocci, Maurizio Vichi and Monia Ranalli ()
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Roberto Rocci: Sapienza University
Maurizio Vichi: Sapienza University
Monia Ranalli: Sapienza University

Computational Statistics, 2025, vol. 40, issue 1, No 19, 469-507

Abstract: Abstract Finite mixture of Gaussians are often used to classify two- (units and variables) or three- (units, variables and occasions) way data. However, two issues arise: model complexity and capturing the true cluster structure. Indeed, a large number of variables and/or occasions implies a large number of model parameters; while the existence of noise variables (and/or occasions) could mask the true cluster structure. The approach adopted in the present paper is to reduce the number of model parameters by identifying a sub-space containing the information needed to classify the observations. This should also help in identifying noise variables and/or occasions. The maximum likelihood model estimation is carried out through an EM-like algorithm. The effectiveness of the proposal is assessed through a simulation study and an application to real data.

Keywords: Three-way data; Cluster analysis; Dimensionality reduction; Mixture models; Tucker2 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01478-1

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