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Probabilistic classification preserving projections and its application to face recognition


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Probabilistic classification preserving projections and its application to face recognition
Yang ZhangjingLiu ChuancaiGu XingjianZhu Jun
School of Computer Science and Engineering,NUST,Nanjing 210094,China
face recognitionfeature extractiondimensionality reductionmanifoldlocality preserving projections
In order to improve the ability of low dimensional space to represent highdimensional samples,a novel manifold learning method called probabilistic classification maintain projection(PCPP) is proposed.The PCPP takes class information into account and refines similarity weights of intraclass samples,which not only contain neighborhood information of samples,but also can reflect the probability that a sample can be correctly classified when its K nearest neighbors are selected.After projection,neighborhood relationship of the intraclass samples which possess more classification probability can be preserved.Experimental results on the Yale,FERET and AR face databases demonstrate that the PCPP performs better than other algorithms.


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Last Update: 2013-02-15