计算机科学 ›› 2012, Vol. 39 ›› Issue (5): 201-204.

• 人工智能 • 上一篇    下一篇

基于L1范数的二维局部保留映射

邢红杰,赵浩鑫   

  1. (河北大学数学与计算机学院河北省机器学习与计算智能重点实验室 保定 071002)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Two-dimensional Locality Preserving Projections Based on L1-norm

  • Online:2018-11-16 Published:2018-11-16

摘要: 提出了一种基于L1范数的二维局部保留映射(two-dimensional locality preserving projections based on L1-norm, 2DLPP-工1)特征提取方法。与传统的基于L2范数的二维局部保留映射(2DLPP)相比,所提方法有两个优点。首先,由于L1范数对噪声不敏感,因此它具有更强的抗噪声能力;其次,它不需要进行特征值分解。在两个人脸数据库和一个手写数字数据集上的实验结果表明,当训练集中有噪声时,所提的2DLPP工1能够取得优于传统2DLPP的分类性能。

关键词: 特征提取,L1范数,局部保留映射,人脸识别

Abstract: This paper presented a method of two-dimensional locality preserving projection based on Ll-norm(2DLPP-Ll). I}he proposed approach has two advantages compared with the conventional I_2-norm based two-dimensional locality preserving projection(2DLPP). Firstly, it is more robust against outliers because Ll-norm is insensitive to noises. Moreover, it does not require the eigenvalue decomposition. Experiments on two face databases and one hand-written digit dataset illustrate that compared with 2DI_PP,the proposed method exhibits better performance when there arc outliers in training sets.

Key words: Feature extraction, I_l-norm, Locality preserving proj ection, Facc recognition

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