New results on multi-dimensional linear discriminant analysis

Amir Beck, Raz Sharon

Research output: Contribution to journalArticlepeer-review

Abstract

Fisher linear discriminant analysis is a well-known technique for dimensionality reduction and classification. The method was first formulated in 1936 by Fisher. In this paper we concentrate on three different formulations of the multi-dimensional problem. We provide a mathematical explanation why two of the formulations are equivalent and prove that this equivalency can be extended to a broader class of objective functions. The second contribution is a rate of convergence of a fixed point method for solving the third model.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalOperations Research Letters
Volume50
Issue number1
DOIs
StatePublished - Jan 2022

Keywords

  • Fixed point methods
  • Generalized eigenvectors
  • Linear discriminant analysis
  • Spectral isotonic functions
  • Superlinear convergence

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