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| Expected Distribution Discriminant Analysis Based on Similarity of Sample Distribution |
| GUO ZhiBo1,2, YANG JingYu1, ZHENG YuJie1, YAN YunYang1 |
1.College of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094
2.College of Information Engineering, Yangzhou University, Yangzhou 225009 |
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Abstract Principal component analysis (PCA) and linear discriminant analysis (LDA) are two kinds of popular feature extraction methods for pattern recognition. A new method, expected distribution discriminant analysis (EDDA), is proposed based on the similarity of sample distribution after some disadvantages of PCA and LDA are indicated. The distribution of extracted features is mostly close to the expected distribution such as idealized distribution by using EDDA. Based on EDDA, the small sample size problem (SSSP) does not occur any more. The dimension of discrimination feature is very low and the recognition performance is enhanced. Some experimental results on ORL and Yale face database demonstrate that the proposed method has higher recognition rate than PCA and LDA.
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Received: 15 September 2006
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2007, Vol. 20 
