Complete large margin linear discriminant analysis using mathematical programming approach

Xiaobo Chen, Jian Yang, Dapeng Zhang, Jun Liang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)


In this paper, we develop a novel dimensionality reduction (DR) framework coined complete large margin linear discriminant analysis (CLMLDA). Inspired by several recently proposed DR methods, CLMLDA constructs two mathematical programming models by maximizing the minimum distance between each class center and the total class center respectively in the null space of within-class scatter matrix and its orthogonal complementary space. In this way, CLMLDA not only makes full use of the discriminative information contained in the whole feature space but also overcome the weakness of linear discriminant analysis (LDA) in dealing with the class separation problem. The solutions of CLMLDA follow from solving two nonconvex optimization problems, each of which is transformed to a series of convex quadratic programming problems by using the constrained concave-convex procedure first, and then solved by off-the-shelf optimization toolbox. Experiments on both toy and several publicly available databases demonstrate its feasibility and effectiveness.
Original languageEnglish
Pages (from-to)1579-1594
Number of pages16
JournalPattern Recognition
Issue number6
Publication statusPublished - 1 Jun 2013


  • Constrained concave-convex procedure
  • Feature extraction
  • Large margin classifier
  • Linear discriminant analysis

ASJC Scopus subject areas

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


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