A Minimum Distance-Based Method for the Classification Problem

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Abstract: In this paper, a kernel fuzzy discriminant analysis minimum distance-based approach for the classifi- cation of face images is proposed to deal with face classification problem (we call this method mdkfda/qr as an abbreviation). A superiority of the mdkfda/qr is its computational efficiency and can avoid the singularity. In the proposed method, the membership degree is incorporated into the definition of between-class and within-class scatter matrixes to get fuzzy between-class and within-class scatter matrixes. The mdkfda/qr approach was com- pared with kernel discriminant analysis (KDA) and fuzzy discriminant analysis (FDA) two algorithms in terms of classification accuracy. Experiments on ORL and FERET two real face datasets are performed to test and evaluate the effectiveness of the proposed algorithm on classification accuracy. The results show that the effect of mdkfda/qr method can achieve higher classification accuracy than KDA and FDA methods.

WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 10, 2014, Art. #61

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Jianqiang Gao, Lizhong Xu, "A Minimum Distance-Based Method for the Classification Problem," WSEAS Transactions on Signal Processing, vol. 10, pp. 592-600, 2014, DOI:
Jianqiang Gao, Lizhong Xu. A Minimum Distance-Based Method for the Classification Problem. WSEAS Transactions on Signal Processing. 2014;10:592-600.
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