Push-Pull marginal discriminant analysis for feature extraction

Zhenghong Gu, Jian Yang, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


Marginal information is of great importance for classification. This paper presents a new nonparametric linear discriminant analysis method named Push-Pull marginal discriminant analysis (PPMDA), which takes full advantage of marginal information. For two-class cases, the idea of this method is to determine projected directions such that the marginal samples of one class are pushed away from the between-class marginal samples as far as possible and simultaneously pulled to the within-class samples as close as possible. This idea can be extended for multi-class cases and give rise to the PPMDA algorithm for feature extraction of multi-class problems. The proposed method is evaluated using the CENPARMI handwritten numeral database, the Extended Yale face database B and the ORL database. Experimental results show the effectiveness of the proposed method and its advantage after performance over the state-of-the-art feature extraction methods.
Original languageEnglish
Pages (from-to)2345-2352
Number of pages8
JournalPattern Recognition Letters
Issue number15
Publication statusPublished - 1 Nov 2010


  • Classification
  • Feature extraction
  • Linear discriminant analysis
  • Nonparametric methods

ASJC Scopus subject areas

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


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