用于分类的样本保局鉴别分析方法

杨利平; 辜小花; 叶洪伟

doi:10.3788/OPE.20111909.2205

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用于分类的样本保局鉴别分析方法

信息科学 | 更新时间：2020-08-12

- 用于分类的样本保局鉴别分析方法
- Sample locality preserving discriminant analysis for classification
- 光学精密工程 2011年19卷第9期页码：2205-2213
- 作者机构：
  
  重庆大学光电技术及系统教育部重点实验室重庆,400044
- 作者简介：
- 基金信息：
  
  中央高校基本科研业务费科研专项项目(No.CDJZR10120010)();高等学校博士学科点专项科研基金资助项目(No.20100191120012)()
- DOI：10.3788/OPE.20111909.2205
  中图分类号： TP391.41
- 收稿日期：2011-01-06，
  
  修回日期：2011-02-27，
  
  网络出版日期：2011-09-26，
  
  纸质出版日期：2011-09-26
- 稿件说明：
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杨利平, 辜小花, 叶洪伟. 用于分类的样本保局鉴别分析方法[J]. 光学精密工程, 2011,19(9): 2205-2213

YANG Li-ping, GU Xiao-hua, YE Hong-wei. Sample locality preserving discriminant analysis for classification[J]. Editorial Office of Optics and Precision Engineering, 2011,19(9): 2205-2213
杨利平, 辜小花, 叶洪伟. 用于分类的样本保局鉴别分析方法[J]. 光学精密工程, 2011,19(9): 2205-2213 DOI： 10.3788/OPE.20111909.2205.

YANG Li-ping, GU Xiao-hua, YE Hong-wei. Sample locality preserving discriminant analysis for classification[J]. Editorial Office of Optics and Precision Engineering, 2011,19(9): 2205-2213 DOI： 10.3788/OPE.20111909.2205.

摘要

针对高维数据分类中鉴别特征降维方法的小样本问题和有效维度丢失问题

结合最新提出的片对齐框架和保局投影提出了样本保局鉴别分析方法。该方法通过分别构造每个样本的类内近邻图和类外近邻图

并将所有样本的类内近邻图和类外近邻图结合起来

形成了所有样本的类内近邻和类外近邻关系。然后

在使所有样本的类内近邻尽可能地聚集在一起的同时使类外近邻尽可能地分开

得到从高维输入空间到低维特征空间的最优映射关系。该方法有效避免了高维数据分类的小样本问题且扩展了鉴别分析的低维特征空间的有效维度。在ORL、FERET和PIE等人脸库上的高维数据分类实验证实

样本保局鉴别分析方法显著优于经典的鉴别特征降维方法。与基于片对齐框架提出的鉴别局部对齐方法相比

样本保局鉴别分析方法在FERET库上的分类识别精度提高了4.5%以上。

Abstract

The small sample size and the loss of effective dimension problems always exist in discriminative dimension reduction methods of high-dimensional data classification. To address these problems

a Sample Locality Preserving Discriminant Analysis (SLPDA) method is proposed by integrating the latest patch alignment framework and Locality Preserving Projections (LPP). The within-class and out-class neighborhood relationships of all samples in the SLPDA are constructed by summing the within-class and out-class neighborhood graphs of each sample

respectively. Thereafter

the optimal mapping from a high-dimensional input space to a low-dimensional feature space of the SLPDA is obtained by making the within-class neighbors of all samples as close as possible and meanwhile keeping the out-class neighbors as distant as possible. The proposed SLPDA method avoids the small sample size problem of high-dimensional data classification and extends the effective dimension of low- dimensional feature space. Experimental results on several high-dimensional face databases

e.g. ORL

FERET and PIE

indicate that the proposed SLPDA method significantly outperforms the classical discriminative dimension reduction methods. Comparing with Discriminative Locality Alignment (DLA)

which is also a dimension reduction method based on patch alignment framework

the recognition rate of SLPDA on a FERET subset is 4.5% higher.

关键词

Keywords

references

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