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基于PCA和LDA统一化原理的增强型?线性鉴别分析准则

DOI: 10.11834/jig.20080418

Keywords: 增强型线性鉴别分析,主分量分析,线性鉴别分析,PCA+LDA

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Abstract:

主分量分析(PCA)和线性鉴别分析(LDA)是模式识别领域的使用最为广泛的两种特征抽取方法,而在图像识别中经常采用的是PCA+LDA方法来代替单纯的LDA。本文提出一种增强型线性鉴别准则(ELDA),将PCA的优点和LDA的优点充分地融合在一起,不仅解决了PCA过程中使用最小距离方法时识别精度相对低的缺点,而且解决了LDA过程中当类内散布矩阵奇异时投影向量的求解问题,也就是说可以使用该方法来替代PCA+LDA的两步骤方法。另外,该方法在识别精度上比PCA和LDA或PCA+LDA方法都有较大的提高,通过在ORL、Yale和NUST603人脸库上的实验验证了该算法的有效性。

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