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2013 | 23 | 4 | 887-903
Tytuł artykułu

A practical application of kernel-based fuzzy discriminant analysis

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A novel method for feature extraction and recognition called Kernel Fuzzy Discriminant Analysis (KFDA) is proposed in this paper to deal with recognition problems, e.g., for images. The KFDA method is obtained by combining the advantages of fuzzy methods and a kernel trick. Based on the orthogonal-triangular decomposition of a matrix and Singular Value Decomposition (SVD), two different variants, KFDA/QR and KFDA/SVD, of KFDA are obtained. In the proposed method, the membership degree is incorporated into the definition of between-class and within-class scatter matrices to get fuzzy between-class and within-class scatter matrices. The membership degree is obtained by combining the measures of features of samples data. In addition, the effects of employing different measures is investigated from a pure mathematical point of view, and the t-test statistical method is used for comparing the robustness of the learning algorithm. Experimental results on ORL and FERET face databases show that KFDA/QR and KFDA/SVD are more effective and feasible than Fuzzy Discriminant Analysis (FDA) and Kernel Discriminant Analysis (KDA) in terms of the mean correct recognition rate.
Rocznik
Tom
23
Numer
4
Strony
887-903
Opis fizyczny
Daty
wydano
2013
otrzymano
2013-03-14
poprawiono
2013-07-10
poprawiono
2013-09-05
Twórcy
  • College of Computer and Information Engineering, Hohai University, Nanjing, 210098, PR China
autor
  • School of Mathematics Sciences, Liaocheng University, Shandong, 252059, PR China
autor
  • Department of Mathematics, Nanjing University of Finance and Economics, Nanjing, 210023, PR China
autor
  • College of Computer and Information Engineering, Hohai University, Nanjing, 210098, PR China
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv23z4p887bwm
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