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| Discriminative Feature Fusion Based on Extensions of PCA |
| TAN Jing-Dong1,2,3, SU Ya-Ru2,3, WANG Ru-Jing2,3 |
1.School of Mathematics,Hefei University of Technology,Hefei 230009
2.Laboratory of Intelligent Decision,Institute of Intelligent Machines,Chinese Academy of Sciences,Hefei 230031
3.Department of Automation,University of Science and Technology of China,Hefei 230027 |
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Abstract Two methods for dimensionality reduction, principal component discriminative analysis and kernel principal component discriminative analysis, are proposed. Based on the theory of principal component analysis and maximum margin criterion, a multi-objective project model is constructed to formalize the goals for feature fusion. Then, it is transformed into a single-objective cost function for the projection, and the optimal linear mapping is obtained through optimizing this cost function. Additionally, the nearly diagonal block kernel matrix is divided into c kernel matrixes (c is the number of classes in dataset), and eigen-decomposition method is used to solve their d principal vectors. Through the process of vector algebra, a combined mapping α is obtained. When the original kernel matrix K is projected on α, the inner-class information is optimally preserved. The experimental results show their validity.
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Received: 11 July 2010
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2012, Vol. 25 
