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基于大间距准则的不相关保局投影分析

DOI: 10.3724/SP.J.1004.2013.01575, PP. 1575-1580

Keywords: 特征提取,大间距准则,保局投影,不相关判别分析,人脸识别

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Abstract:

?局部保持投影(Localitypreservingprojections,LPP)算法只保持了目标在投影后的邻域局部信息,为了更好地刻画数据的流形结构,引入了类内和类间局部散度矩阵,给出了一种基于有效且稳定的大间距准则(Maximummargincriterion,MMC)的不相关保局投影分析方法.该方法在最大化散度矩阵迹差时,引入尺度因子α,对类内和类间局部散度矩阵进行加权,以便找到更适合分类的子空间并且可避免小样本问题;更重要的是,大间距准则下提取的判别特征集一般情况下是统计相关的,造成了特征信息的冗余,因此,通过增加一个不相关约束条件,利用推导出的公式提取不相关判别特征集,这样做,对正确识别更为有利.在Yale人脸库、PIE人脸库和MNIST手写数字库上的测试结果表明,本文方法有效且稳定,与LPP、LDA(Lineardiscriminantanalysis)和LPMIP(Locality-preservedmaximuminformationprojection)方法等相比,具有更高的正确识别率.

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