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| Kernel Orthogonal Discriminant Local Tangent Space Alignment Algorithm |
| ZHENG Gang-Min,XIA Su-Na,MA Yuan-Yuan,MA Xiao-Hu |
| School of Computer Science and Technology,Soochow University,Suzhou 215006 |
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Abstract To address the drawbacks of the local tangent space alignment algorithm,a feature extraction method based on kernel transformation,kernel orthogonal discriminant local tangent space alignment algorithm (KOTSDA),is proposed. Firstly,the kernel mapping is performed to map the face data into a high dimensional nonlinear space and extract the nonlinear information.Then,tangent space discriminant analysis algorithm is used to preserve the intra-class local geometric structures and simultaneously maximize the inter-class difference in target function. Finally,KOTSDA is obtained with orthogonal constraints. It effectively avoids losing discriminant information which does not need to preprocess by PCA dimensional reduction. The experiments on ORL and Yale face databases demonstrate the effectiveness of the proposed algorithm.
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Received: 03 July 2012
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2013, Vol. 26 
