On improvement of multiple discriminant analysis method for discriminative feature extraction

Author

Gao, Jiang ; Ding, Xiaoqing ; Wu, Youshou

Author_Institution

Dept. of Electron. Eng., Tsinghua Univ., Beijing, China

Volume

2

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

915

Abstract

Two new algorithms-modified multiple discriminant analysis (MMDA) and difference principle component analysis (DPCA)-are presented and derived. The proposed algorithms are especially useful in automatic feature extraction for extremely similar patterns in a small category set. Experimental results for recognition of Chinese character fonts and handwritten numerals using MMDA and DPCA are presented. Compared with the traditional algorithms, MMDA and DPCA avoid the computational stability problem inherent in multiple discriminant analysis (MDA), and provide more effective feature metrics for pattern discrimination

Keywords

feature extraction; handwritten character recognition; principal component analysis; Chinese character fonts; category set; computational stability; difference principle component analysis; discriminative feature extraction; feature metrics; handwritten numerals; multiple discriminant analysis method; pattern discrimination; Algorithm design and analysis; Analysis of variance; Character recognition; Feature extraction; Handwriting recognition; Image processing; Pattern analysis; Principal component analysis; Shape; Stability analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on

Conference_Location

Tokyo

ISSN

1062-922X

Print_ISBN

0-7803-5731-0

Type

conf

DOI

10.1109/ICSMC.1999.825384

Filename

825384