Partial least squares for discrimination

Matthew Barker, William Rayens

Research output: Contribution to journalArticlepeer-review

2215 Scopus citations


Partial least squares (PLS) was not originally designed as a tool for statistical discrimination. In spite of this, applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role. The interesting question is: why can a procedure that is principally designed for overdetermined regression problems locate and emphasize group structure? Using PLS in this manner has heurestic support owing to the relationship between PLS and canonical correlation analysis (CCA) and the relationship, in turn, between CCA and linear discriminant analysis (LDA). This paper replaces the heuristics with a formal statistical explanation. As a consequence, it will become clear that PLS is to be preferred over PCA when discrimination is the goal and dimension reduction is needed.

Original languageEnglish
Pages (from-to)166-173
Number of pages8
JournalJournal of Chemometrics
Issue number3
StatePublished - Mar 1 2003


  • Dimension reduction
  • Linear discrimination
  • Partial least squares

ASJC Scopus subject areas

  • Analytical Chemistry
  • Applied Mathematics


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