Supervised graph-optimized locality preserving projections

GU Xiao-hua; GONG Wei-guo; YANG Li-ping

doi:10.3788/OPE.20111903.0672

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Supervised graph-optimized locality preserving projections

Article | 更新时间：2020-08-12

- Supervised graph-optimized locality preserving projections
- Optics and Precision Engineering Vol. 19, Issue 3, Pages: 672-680(2011)
- 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：
- DOI：10.3788/OPE.20111903.0672
  CLC： TP391.4
- Received：26 June 2010，
  
  Revised：07 July 2010，
  
  Published Online：22 March 2011，
  
  Published：22 March 2011
- 稿件说明：
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辜小花, 龚卫国, 杨利平. 有监督图优化保局投影[J]. 光学精密工程, 2011,19(3): 672-680

GU Xiao-hua, GONG Wei-guo, YANG Li-ping. Supervised graph-optimized locality preserving projections[J]. Editorial Office of Optics and Precision Engineering, 2011,19(3): 672-680
辜小花, 龚卫国, 杨利平. 有监督图优化保局投影[J]. 光学精密工程, 2011,19(3): 672-680 DOI： 10.3788/OPE.20111903.0672.

GU Xiao-hua, GONG Wei-guo, YANG Li-ping. Supervised graph-optimized locality preserving projections[J]. Editorial Office of Optics and Precision Engineering, 2011,19(3): 672-680 DOI： 10.3788/OPE.20111903.0672.

摘要

研究了保局投影中近邻图的构造及更新问题

提出了一种有监督图优化保局投影(SGoLPP)特征提取方法

并应用于人脸识别。不同于传统的保局投影(LPP)算法预先设定权值矩阵并通过一次优化求解投影矩阵

SGoLPP将权值矩阵作为学习项引入到目标函数

通过交替迭代更新逐步获得最优权值矩阵和最优投影矩阵。同时

通过引入类别信息

始终对同类样本点对的权值进行更新

有效地抑制了异类样本的干扰。在UCI模拟数据集上

SGoLPP在较少的迭代次数下获得了更好的聚类和分类效果。在Yale

UMIST和CMU PIE人脸库上

SGoLPP的平均识别率比LPP、有监督保局投影(SLPP)和图优化保局投影(GoLPP)分别高出26.6%、4.8%和8.8%。实验显示本文提出的SGoLPP算法在样本可分性与鲁棒性方面具有优势

可有效地提取人脸特征。

Abstract

This paper focuses on the construction and optimization of neighbour graph and proposes a Supervised Graph-optimized Locality Preserving Projections (SGoLPP) method for facial feature extraction. Different from the Locality Preserving Projections(LPP) that it predefines the weight matrix and solves the projection matrix by one step optimization

the SGoLPP incorporates the weight matrix into the objective function as a learning term

and optimizes the weight matrix and projection matrix simultaneously. Meanwhile

the label information is utilized to update the weights corresponding to sample pairs in the same class and to avoid the interferences from samples not in the same class. Experiments on the Wine database of UCI show that the SGoLPP achieves better cluster performance with less iterations. For face recognition

the average recognition accuracies of SGoLPP on Yale

UMIST and CMU PIE face databases are 26.6%

4.8% and 8.8% higher than those of LPP

Supervised Locality Preserving Projections(SLPP) and Graph-optimized Locality Preserving Projections(GoLPP)

respectively

which verifies the effectiveness and superiority of the proposed method.

关键词

Keywords

references

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