3.29 - Statistical Discriminant Analysis

B. K. Lavine, W. S. Rayens

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

Canonical discriminant analysis (CDA) and linear discriminant analysis (LDA) are popular classification techniques. Likewise, practitioners, who are familiar with regularized discriminant analysis (RDA), soft modeling by class analogy (SIMCA), principal component analysis (PCA), and partial least squares (PLS) will often use them to perform classification. In this chapter, we will attempt to make some sense out of all of this. We will explain when CDA and LDA are the same and when they are not the same. We will also discuss the relative merits of the various stabilization and dimension reducing methods used, focusing on RDA for numerical stabilization of the inverse of the covariance matrix and PCA and PLS as part of a two-step process for classification when dimensionality reduction is an issue.

Original languageEnglish
Title of host publicationComprehensive Chemometrics
Subtitle of host publicationChemical and Biochemical Data Analysis, Second Edition: Four Volume Set
Pages585-603
Number of pages19
Volume3
ISBN (Electronic)9780444641656
DOIs
StatePublished - Jan 1 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V. All rights reserved

Keywords

  • SIMCA
  • canonical discriminant analysis
  • discriminant PLS
  • linear discriminant analysis
  • oriented PLS
  • quadratic discriminant analysis
  • regularized discriminant analysis

ASJC Scopus subject areas

  • General Chemistry

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