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| Supervised Locality Preserving Canonical Correlation Analysis Algorithm |
| HOU Shu-Dong, SUN Quan-Sen, XIA De-Shen |
| School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094 |
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Abstract To use locality preserving canonical correlation analysis (LPCCA) in pattern classification and acquire fine results, a supervised locality preserving canonical correlation analysis (SLPCCA) is proposed based on LPCCA incorporated the class label information. Through maximizing the weighted correlation between corresponding samples and their near neighbors belonging to the same classes, SLPCCA effectively utilizes the class label information and preserves the local manifold structure of the data. In addition, the proposed algorithm effectively fuses the discrimination information of DCCA without the restriction of total class numbers. Besides, a kernel SLPCCA (KSLPCCA) is also proposed based on kernel methods to extract nonlinear features of the data. The experimental results on ORL, Yale, AR and FERET face databases show that the proposed algorithms are better than related canonical correlation analysis methods.
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Received: 07 June 2010
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2012, Vol. 25 
