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  2011, Vol. 24 Issue (5): 658-664    DOI:
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Graph-Optimized Linear Discriminant Projection and Its Application to Image Recognition
YIN Jun, JIN Zhong
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094

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Abstract  The class information of the data is sufficiently utilized and a feature extraction algorithm is proposed called graph-optimized linear discriminant projection (GoLDP) based on graph-optimized locality preserving projection (GoLPP). The graph of GoLDP is constructed by optimizing an objective function, which is similar to GoLPP. GoLDP constructs two optimal graphs (optimal intrinsic graph and optimal penalty graph) by using class information, which is different from GoLPP, and obtains the optimal projection matrix according to these two optimal graphs. Experimental results on FERET and YALE face databases and the PolyU palmprint database demonstrate the effectiveness of GoLDP.
Key wordsFeature Extraction      Locality Preserving Projection      Graph-Optimized      Face Recognition      Palmprint Recognition     
Received: 15 September 2010     
ZTFLH: TP391.41  
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YIN Jun
JIN Zhong
Cite this article:   
YIN Jun,JIN Zhong. Graph-Optimized Linear Discriminant Projection and Its Application to Image Recognition[J]. , 2011, 24(5): 658-664.
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