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| Combined-Feature-Discriminability Enhanced Canonical Correlation Analysis |
| ZHOU Xu-Dong1,2, CHEN Xiao-Hong3, CHEN Song-Can1,4 |
1.College of Computer Science and Technology,Nanjing University of Aeronautics Astronautics,Nanjing 210016
2.Information Engineering College,Yangzhou University,Yangzhou 225009
3.College of Science,Nanjing University of Aeronautics Astronautics,Nanjing 210016
4.National Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210093 |
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Abstract Canonical Correlation Analysis (CCA) has following two deficiencies in performing classification task: CCA can not directly optimize them but their components, though combined features are the input of the classifier; CCA can not utilize any class information at all, though facing classification task. To overcome these deficiencies, a supervised dimension reduction method named combined-feature-discriminability enhanced canonical correlation analysis (CECCA) is proposed. CECCA is developed through incorporating discriminant analysis of combined features into CCA. Consequently, it optimizes the combined feature correlation and discriminability simultaneously and thus makes the extracted features more suitable for classification. The experimental results on artificial dataset, multiple feature database and facial databases show that the proposed method is effective.
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Received: 13 October 2010
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2012, Vol. 25 
