LDA/QR: An efficient and effective dimension reduction algorithm and its theoretical foundation

Jieping Ye, Qi Li

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

LDA/QR, a linear discriminant analysis (LDA) based dimension reduction algorithm is presented. It achieves the efficiency by introducing a QR decomposition on a small-size matrix, while keeping competitive classification accuracy. Its theoretical foundation is also presented.

Original languageEnglish (US)
Pages (from-to)851-854
Number of pages4
JournalPattern Recognition
Volume37
Issue number4
DOIs
StatePublished - Apr 2004

Keywords

  • Linear discriminant analysis
  • Pseudo-inverse
  • QR-decomposition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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