Model-Based Clustering and Classification via Patterned Covariance Analysis

Luca Bagnato, Francesca Greselin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work deals with the classification problem in the case that groups are known and both labeled and unlabeled data are available. The classification rule is derived using Gaussian mixtures, with covariance matrices fixed according to a multiple testing procedure, which allows to choose among four alternatives: heteroscedasticity, homometroscedasticity, homotroposcedasticity, and homoscedasticity. The mixture models are then fitted using all available data (labeled and unlabeled) and adopting the EM and the CEM algorithms. Applications on real data are provided in order to show the classification performance of the proposed procedure.
Original languageEnglish
Title of host publicationCLADAG 2011 - Book of Abstracts
Pages4
Number of pages1
Publication statusPublished - 2011
EventCLAssification and Data Analysis Group of the Italian Statistical Society, 8th Scientific Meeting University of Pavia - Pavia
Duration: 7 Sep 20119 Sep 2011

Conference

ConferenceCLAssification and Data Analysis Group of the Italian Statistical Society, 8th Scientific Meeting University of Pavia
CityPavia
Period7/9/119/9/11

Keywords

  • Cluster Analysis
  • Discriminant Analysis

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