On the Spectral Decomposition in Normal Discriminant Analysis

Luca Bagnato, Francesca Greselin, Antonio Punzo

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

This paper enlarges the covariance configurations, on which the classical linear discriminant analysis is based, by considering the four models arising from the spectral decomposition when eigenvalues and/or eigenvectors matrices are allowed to vary or not between groups. Similarly to the classical approach, the assessment of these configurations is accomplished via a test on the training set. The discrimination rule is then built upon the configuration provided by the test, considering or not the unlabelled data. Numerical experiments, on simulated and real data, have been performed to evaluate the gain of our proposal with respect to the linear discriminant analysis.
Original languageEnglish
Pages (from-to)1471-1489
Number of pages19
JournalCOMMUNICATIONS IN STATISTICS. SIMULATION AND COMPUTATION
Volume43
DOIs
Publication statusPublished - 2014

Keywords

  • CEM algorithm
  • EM algorithm
  • Mixture models
  • Normal discriminant analysis

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