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On Improvement of Feature Extraction Algorithms for Discriminative Pattern Classification
Barcelona, Spain September 03-September 08
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.90602615th International Conference on Patt ...
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Two new feature extraction strategies-modified multiple discriminant analysis (MMDA) and difference principal component analysis (DPCA)-are presented and derived. The proposed algorithms are especially useful in automatic feature extraction from 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 provide more effective feature metrics for pattern discrimination in some settings.
Citation:
Jiang Gao, Xiaoqing Ding, "On Improvement of Feature Extraction Algorithms for Discriminative Pattern Classification," icpr, vol. 2, pp.2101, 15th International Conference on Pattern Recognition (ICPR'00) - Volume 2, 2000
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