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By normalizing to improve generalized Foley-Sammon transform in high-dimensional spaces - with application to face recognition

机译：通过归一化以改进高维空间中的广义Foley-Sammon变换-应用于人脸识别

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摘要

Linear discriminant analysis (LDA) is an effective feature extraction technique for classification. A new LDA-based algorithm, i.e., direct normalized generalized Foley-Sammon transform (DN-GFST) method in high dimensional spaces, is presented in this paper. It not only overcomes the limitation of traditional LDA that they overemphasize the larger distance between classes and cause large overlaps of neighboring classes, but also has the best separable ability in global sense. Lastly, our method is applied to facial image recognition, and the experimental results show that the performance of the present method is superior to those of the existing methods in terms of the classification error rate.
机译：线性判别分析（LDA）是一种有效的分类特征提取技术。本文提出了一种新的基于LDA的算法，即在高维空间中的直接归一化广义Foley-Sammon变换（DN-GFST）方法。它不仅克服了传统LDA的局限性，即它们过分强调类之间的较大距离并导致相邻类的大量重叠，而且在全局意义上具有最佳的可分离性。最后，将我们的方法应用于人脸图像识别，实验结果表明，该方法在分类错误率方面优于现有方法。

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