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小样本情况下Fisher线性鉴别分析的理论及其验证

DOI: 10.11834/jig.200508183

Keywords: 小样本问题,主成分分析,线性鉴别分析,压缩变换,人脸识别

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Abstract:

线性鉴别分析是特征抽取中最为经典和广泛使用的方法之一。近几年,在小样本情况下如何抽取Fisher最优鉴别特征一直是许多研究者关心的问题。本文应用投影变换和同构变换的原理,从理论上解决了小样本情况下最优鉴别矢量的求解问题,即最优鉴别矢量可在一个低维空间里求得;给出了特征抽取模型,并给出求解模型的PPCA+LDA算法;在ORL人脸库3种分辨率灰度图像上进行实验。实验结果表明,PPCA+LDA算法抽取的鉴别向量有较强的特征抽取能力,在普通的最小距离分类器下能达到较高的正确识别率,而且识别结果十分稳定。

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