Adaptive classification algorithm based on maximum scatter difference discriminant criterion

Feng Xi Song, Dapeng Zhang, Jing Yu Yang, Xiu Mei Gao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


In this paper we first prove that the optimal discriminant direction of Maximum scatter difference (MSD) discriminant criterion with a certain value c0is equivalent to the optimal Fisher discriminant direction. Second, sample recognition rate curves of MSD are illustrated. The recognition rate curve is usually a pulse curve when the within-class scatter matrix is nonsingular. With the increase of parameter C, the recognition rate of MSD also increases. The recognition rate of MSD achieves its maximum when C is equal to c0. In addition, former study showed that, when the within-class scatter matrix is singular, MSD criterion is approaching the large margin linear projection criterion as parameter C increases. Moreover, the recognition rate curve of MSD is non-decreasing. Thus, an adaptive classification algorithm based on maximum scatter difference discriminant criterion is proposed based on these facts. The new algorithm can tune parameter C automatically according to the characteristics of training samples. Experiment conducted on 6 datasets from UCI Machine Learning Repository and AR face database demonstrates that the adaptive classification algorithm for maximum scatter difference has good classification property.
Original languageChinese (Simplified)
Pages (from-to)541-549
Number of pages9
JournalZidonghua Xuebao/Acta Automatica Sinica
Issue number4
Publication statusPublished - 1 Jul 2006


  • Adaptive algorithm
  • Face recognition
  • Fisher discriminant criterion
  • Large margin linear projection
  • Machine learning
  • Maximum scatter difference

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Computer Graphics and Computer-Aided Design

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