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基于核邻域保持投影的人脸识别 被引量:15

Kernel Neighborhood Preserving Projections for Face Recognition
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摘要 提出了一种有效的非线性子空间学习方法:核邻域保持投影.其主要思想是通过引入线性变换矩阵来近似经典的局部线性嵌入(LLE),然后通过核方法的技巧在高维空间里求解.经过推导,实际的子空间的计算可归结为标准的特征值分解问题而非推广的特征值分解问题.在AR人脸数据库上的试验表明该方法是有效的. An efficient nonlinear subspace learning method, kernel neighborhood preserving projections (KNPP), is developed. The main idea is to approximate the classical local linear embedding (LLE) by inlxoducing a linear transformation matrix and then find the solution in a very high dimensional space by kernel trick. The actual computation of the subspace is reduced to a standard eignenvalue problem rather than the generalized one. Experiments on AR face database demonstrate the effectiveness of the proposed method.
出处 《电子学报》 EI CAS CSCD 北大核心 2006年第8期1542-1544,共3页 Acta Electronica Sinica
基金 国家自然科学基金(No.60572067) 国家模式识别实验室2005年度开放课题
关键词 人脸识别 子空间学习 核方法 降维 face recognition subspace learning kernel methods dimension reduction
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参考文献9

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二级参考文献69

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