Model-Based Classification Via Patterned Covariance Analysis

Research output: Chapter in Book/Report/Conference proceedingChapter

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 where covariance matrices are given according to a multiple testing procedure which asesses a pattern among 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. The performance of the proposed procedure is evaluated by a simulation study.
Original languageEnglish
Title of host publicationStatistical Models for Data Analysis
EditorsPAOLO GIUDICI, SALVATORE INGRASSIA, MAURIZIO VICHI
Pages17-26
Number of pages10
Publication statusPublished - 2013

Keywords

  • Discriminant analysis
  • Mixtures

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