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

Author

Listed:
  • Roberto Rocci

    (Sapienza University)

  • Maurizio Vichi

    (Sapienza University)

  • Monia Ranalli

    (Sapienza University)

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.

Suggested Citation

  • Roberto Rocci & Maurizio Vichi & Monia Ranalli, 2025. "Mixture models for simultaneous classification and reduction of three-way data," Computational Statistics, Springer, vol. 40(1), pages 469-507, January.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01478-1
    DOI: 10.1007/s00180-024-01478-1
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