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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.
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Received: 15 September 2010
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2011, Vol. 24 
