Model-Based Clustering and Classification via Patterned Covariance Analysis

Luca Bagnato, Francesca Greselin

Risultato della ricerca: Contributo in libroContributo a convegno

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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteCLADAG 2011 - Book of Abstracts
Pagine4
Numero di pagine1
Stato di pubblicazionePubblicato - 2011
EventoCLAssification and Data Analysis Group of the Italian Statistical Society, 8th Scientific Meeting University of Pavia - Pavia
Durata: 7 set 20119 set 2011

Convegno

ConvegnoCLAssification and Data Analysis Group of the Italian Statistical Society, 8th Scientific Meeting University of Pavia
CittàPavia
Periodo7/9/119/9/11

Keywords

  • Cluster Analysis
  • Discriminant Analysis

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