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基于鉴别稀疏保持嵌入的人脸识别算法

DOI: 10.3724/SP.J.1004.2014.00073, PP. 73-82

Keywords: 人脸识别,稀疏表示,稀疏保持投影,鉴别稀疏保持嵌入

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Abstract:

?鉴于近年来稀疏表示(Sparserepresentation,SR)在高维数据例如人脸图像的特征提取与降维领域的快速发展,对原始的稀疏保持投影(Sparsitypreservingprojection,SPP)算法进行了改进,提出了一种叫做鉴别稀疏保持嵌入(Discriminantsparsitypreservingembedding,DSPE)的算法.通过求解一个最小二乘问题来更新SPP中的稀疏权重并得到一个更能真实反映鉴别信息的鉴别稀疏权重,最后以最优保持这个稀疏权重关系为目标来计算高维数据的低维特征子空间.该算法是一个线性的监督学习算法,通过引入鉴别信息,能够有效地对高维数据进行降维.在ORL库、Yale库、扩展YaleB库和CMUPIE库上的大量实验结果验证了算法的有效性.

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