Matrix pattern based minimum within-class scatter support vector machines

Gao Jun, Fu Lai Korris Chung, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Based on minimum within-class scatter support vector machines (MCSVM), a new matrix pattern based MCSSVM (MCSVMmatrix) is presented. Accordingly, it is extended by introducing Mercer's kernels in order to solve the problem of nonlinear decision boundaries, which presents a significant matrix pattern based nonlinear support vector machines: Ker-MCSVMmatrix. The above-mentioned approaches not only keep the merits of MCSVM, but, owing to introducing matrix pattern based within-class scatter matrix into support vector machines, theoretically better solve the singular problem of within-class scatter matrix when small sample size problems are dealt with, reduce the time/place complexity when within-class scatter matrix, its invertible matrix and weight vector ω are calculated. Hence, the classification accuracy is improved to certain extent. Experimental results indicate the above advantages of the proposed methods: both MCSVMmatrixand Ker-MCSVMmatrix.
Original languageEnglish
Pages (from-to)5602-5610
Number of pages9
JournalApplied Soft Computing Journal
Volume11
Issue number8
DOIs
Publication statusPublished - 1 Dec 2011

Keywords

  • Face recognition
  • Matrix pattern
  • SVM
  • Within-class scatter matrix

ASJC Scopus subject areas

  • Software

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