Abstract:

From the angle of model recognition, based on Canonical Correlation Analysis (CCA) we propose a new supervised locality preserving canonical correlation analysis (SALPCCA) based on the ALPCCA. By leveraging the useful information of class label, we can expediently construct the nearest neighbor graph and build multi-weighted correlation between samples. Through maximizing the weighted correlation between corresponding samples and their near neighbors belonging to the same classes, the SALPCCA effectively utilizes the class label information and preserves the local manifold structure of the data. Besides, we also propose a kernel SALPCCA (KSALPCCA) based on the kernel methods to better extract the nonlinear features of the data. The experimental results on the face databases of ORL, Yale and AR show that the proposed algorithm has better performance compared with the traditional canonical correlation analysis methods.

Key words: locality preserving;canonical correlation analysis(CCA);feature extraction;face recognition