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An Efficient Feature Extraction Method, Global Between Maximum and Local Within Minimum, and Its Applications

机译：一种有效的全局最大与最小局部之间的特征提取方法及其应用

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摘要

Feature extraction plays an important role in preprocessing procedure in dealing with small sample size problems. Considering the fact that LDA, LPP, and many other existing methods are confined to one case of the data set. To solve this problem, we propose an efficient method in this paper, named global between maximum and local within minimum. It not only considers the global structure of the data set, but also makes the best of the local geometry of the data set through dividing the data set into four domains. This method preserves relations of the nearest neighborhood, as well as demonstrates an excellent performance in classification. Superiority of the proposed method in this paper is manifested in many experiments on data visualization, face representative, and face recognition.
机译：特征提取在处理小样本问题的预处理过程中起着重要作用。考虑到以下事实：LDA，LPP和许多其他现有方法仅限于数据集的一种情况。为了解决这个问题，我们在本文中提出了一种有效的方法，该方法称为最大值之间的全局和最小值内的局部。它不仅考虑数据集的全局结构，而且通过将数据集划分为四个域来充分利用数据集的局部几何形状。该方法保留了最近邻域的关系，并在分类中表现出出色的性能。本文提出的方法的优越性在数据可视化，面部代表和面部识别的许多实验中得到体现。

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  • 来源
    《Mathematical Problems in Engineering》 |2011年第3期|p.1-15|共15页
  • 作者

    Lei Wang; Jiangshe Zhang; Fei Zang;

  • 作者单位

    School of Science, Xi'an Jiaotong University, Xi'an 710049, China,State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    School of Science, Xi'an Jiaotong University, Xi'an 710049, China,State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    School of Science, Xi'an Jiaotong University, Xi'an 710049, China,State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

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