From classifiers to discriminators: A nearest neighbor rule induced discriminant analysis

Jian Yang, Lei Zhang, Jing Yu Yang, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

49 Citations (Scopus)

Abstract

The current discriminant analysis method design is generally independent of classifiers, thus the connection between discriminant analysis methods and classifiers is loose. This paper provides a way to design discriminant analysis methods that are bound with classifiers. We begin with a local mean based nearest neighbor (LM-NN) classifier and use its decision rule to supervise the design of a discriminator. Therefore, the derived discriminator, called local mean based nearest neighbor discriminant analysis (LM-NNDA), matches the LM-NN classifier optimally in theory. In contrast to that LM-NNDA is a NN classifier induced discriminant analysis method, we further show that the classical Fisher linear discriminant analysis (FLDA) is a minimum distance classifier (i.e. nearest Class-mean classifier) induced discriminant analysis method. The proposed LM-NNDA method is evaluated using the CENPARMI handwritten numeral database, the NUST603 handwritten Chinese character database, the ETH80 object category database and the FERET face image database. The experimental results demonstrate the performance advantage of LM-NNDA over other feature extraction methods with respect to the LM-NN (or NN) classifier.
Original languageEnglish
Pages (from-to)1387-1402
Number of pages16
JournalPattern Recognition
Volume44
Issue number7
DOIs
Publication statusPublished - 1 Jul 2011

Keywords

  • Classification
  • Classifier
  • Dimensionality reduction
  • Discriminant analysis
  • Feature extraction
  • Pattern recognition

ASJC Scopus subject areas

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

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