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  2013, Vol. 26 Issue (7): 673-679    DOI:
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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.
Key wordsFeature Extraction      Local Tangent Space Alignment      Kernel Space      Manifold Learning     
Received: 03 July 2012     
ZTFLH: TP391.4  
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ZHENG Gang-Min
XIA Su-Na
MA Yuan-Yuan
MA Xiao-Hu
Cite this article:   
ZHENG Gang-Min,XIA Su-Na,MA Yuan-Yuan等. Kernel Orthogonal Discriminant Local Tangent Space Alignment Algorithm[J]. , 2013, 26(7): 673-679.
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