Supervised Locality Preserving Canonical Correlation Analysis
Songcan Chen · Journal of Chinese Computer Systems · 2010
In this paper,a novel supervised locality preserving canonical correlation analysis(SLPCCA)is developed,which uses discriminative structural information to construct the class-information matrix,as well as combine the correlation of the neighboring samples to construct the similarity matrix.As a result,SLPCCA can not only improve the ability of CCA to solve nonlinear problems by infusing the local structural information and breaking the linear restriction,but also overcome the shortcoming of LPCCA which neglects the class information.We obtain features which is more favor of classification compared with LPCCA.The experimental results on Multiple Feature Dataset and USPS Dataset have demonstrated the superiority of our proposed SLPCCA compared with CCA and LPCCA.