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Journal of Classification

, Volume 34, Issue 1, pp 4–34 | Cite as

Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models

  • Utkarsh J. DangEmail author
  • Antonio Punzo
  • Paul D. McNicholas
  • Salvatore Ingrassia
  • Ryan P. Browne
Article

Abstract

A family of parsimonious Gaussian cluster-weighted models is presented. This family concerns a multivariate extension to cluster-weighted modelling that can account for correlations between multivariate responses. Parsimony is attained by constraining parts of an eigen-decomposition imposed on the component covariance matrices. A sufficient condition for identifiability is provided and an expectation-maximization algorithm is presented for parameter estimation. Model performance is investigated on both synthetic and classical real data sets and compared with some popular approaches. Finally, accounting for linear dependencies in the presence of a linear regression structure is shown to offer better performance, vis-à-vis clustering, over existing methodologies.

Keywords

Cluster-weighted model EM algorithm Multivariate response Modelbased clustering Mixture models Parsimonious models Eigen-decomposition Regression 

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Copyright information

© Classification Society of North America 2017

Authors and Affiliations

  • Utkarsh J. Dang
    • 1
    Email author
  • Antonio Punzo
    • 2
  • Paul D. McNicholas
    • 3
  • Salvatore Ingrassia
    • 2
  • Ryan P. Browne
    • 4
  1. 1.Department of Mathematical SciencesBinghamton University, State University of New YorkBinghamtonUSA
  2. 2.University of CataniaCataniaItaly
  3. 3.McMaster UniversityOntarioCanada
  4. 4.University of WaterlooOntarioCanada

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