基于稀疏重构的判别分析

doi:10.11772/j.issn.1001-9081.2014.06.1608

计算机应用 ›› 2014, Vol. 34 ›› Issue (6): 1608-1612.DOI: 10.11772/j.issn.1001-9081.2014.06.1608

基于稀疏重构的判别分析

齐鸣鸣¹,²,向阳²

1. 绍兴文理学院元培学院信息与电子系,浙江绍兴312000)
2. 同济大学计算机科学与技术系,上海 201804

收稿日期:2013-11-15 修回日期:2014-01-18 出版日期:2014-06-01 发布日期:2014-07-02
通讯作者: 齐鸣鸣
作者简介:齐鸣鸣(1974-),男,江西景德镇人,讲师,博士研究生,主要研究方向:机器学习、图像处理;向阳(1962-),男,重庆人,教授,博士生导师,博士,主要研究方向:机器学习、语义分析。
基金资助:
国家自然科学基金资助项目;绍兴文理学院校科研项目

Sparsity reconstruction-based discriminant analysis

QI Mingming¹,²,XIANG Yang¹

1. Department of Computer Science and Technology, Tongji University, Shanghai 201804, China;
2. Department of Information and Electronic, Shaoxing University Yuanpei College, Shaoxing Zhejiang 312000, China

Received:2013-11-15 Revised:2014-01-18 Online:2014-06-01 Published:2014-07-02
Contact: QI Mingming

摘要/Abstract

摘要：

为了解决现有判别分析算法对残缺和遮挡等外部干扰比较敏感的问题,从局部稀疏表示的角度,提出一种基于稀疏重构的判别分析(SDA)降维算法。该算法首先利用稀疏表示完成各个类内局部稀疏重构,然后通过非所在类内的样本均值完成各样本的类间局部稀疏重构,最后在降维过程中保持类间和类内的稀疏重构信息之比。在AR和UMIST人脸库人脸数据集上的实验结果表明,与基于图优化的Fisher分析(GbFA)算法和基于重构判别分析(RDA)算法相比,该算法提高了基于近邻分类的最高识别准确率2%~10%。

Abstract:

In order to solve the problem of being sensitive to external interference such as defects and occlusions in the existing discriminant analysis, a Sparsity reconstruction-based Discriminant Analysis (SDA) for dimensionality reduction was proposed in the term of local sparse representation. The algorithm firstly made use of sparse representation to complete local sparsity reconstruction in each class, and then completed between-class sparsity reconstruction with the average of each different class. Finally the algorithm preserved the ratio between the between-class sparsity reconstruction information and the within-class sparsity reconstruction information in the process of dimensionality reduction. The algorithm promotes the computational efficiency of sparse representation and the robust performance of discriminant analysis. The experimental results on AR and UMIST face datasets show, compared with Graph-based Fisher Analysis (GbFA) algorithm and Reconstructive-based Discriminant Analysis (RDA) algorithm, the proposed algorithm promotes 2-10 percent in the highest recognition accuracy based on nearest neighbor classification.

中图分类号:

TP391.41

齐鸣鸣向阳. 基于稀疏重构的判别分析[J]. 计算机应用, 2014, 34(6): 1608-1612.

QI Mingming XIANG Yang. Sparsity reconstruction-based discriminant analysis[J]. Journal of Computer Applications, 2014, 34(6): 1608-1612.

参考文献

[1]MIKA S. Kernel Fisher discriminant[EB/OL].[2013-10-10]. http://opus4.kobv.de/opus4-tuberlin/frontdoor/deliver/index/docId/482/file/mika_sebastian.pdf.
[2]JI S, YE J. Generalized linear discriminant analysis: a unified framework and efficient model selection[J]. IEEE Transactions on Neural Networks,2008,19(10):1768-1782.
[3]LU J, PLATANIOTIS K N, VENETSANOPOULOS A N. Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition[J]. Pattern Recognition Letters, 2005, 26(2):181-191.
[4]HASTIE T, BUJA A, TIBSHIRANI R. Penalized discriminant analysis[J]. The Annals of Statistics,1995,23(1):73-102.
[5]HOWLAND P, PARK H. Generalizing discriminant analysis using the generalized singular value decomposition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(8):995-1006.
[6]YE J, LI Q. A two-stage linear discriminant analysis via QR-decomposition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(6):929-941.
[7]YE J. Computational and theoretical analysis of null space and orthogonal linear discriminant analysis[J]. Journal of Machine Learning Research,2006,7:1183-1204.
[8]XU B, HUANG K, LIU C L. Maxi-min discriminant analysis via online learning[J].Neural Networks,2012,34(10):56-64.
[9]LU G F, ZOU J, WANG Y. Incremental complete LDA for face recognition[J]. Pattern Recognition, 2012,45(7):2510-2521.
[10]CUI Y, FAN L Y. A novel supervised dimensionality reduction algorithm: graph-based fisher analysis[J]. Pattern Recognition,2012,45(4):1471-1481.
[11]CHEN Y, JIN Z. Reconstructive discriminant analysis: a feature extraction method induced from linear regression classification[J]. Neurocomputing, 2012,87(15):41-50.
[12]CHENG B, YANG J, YAN S, et al. Learning with l(1)-graph for image analysis [J]. IEEE Transactions on Image Processing,2010, 19(4):858-866.
[13]WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):210-227.
[14]YUAN X, YAN S. Visual classification with multi-task joint sparse representation[C]// Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2010: 3493-3500.
[15]QIAO L S, CHEN S C, TAN X Y. Sparsity preserving projections with applications to face recognition [J]. Pattern Recognition, 2010, 43(1):331-341.
[16]GUI J, SUN Z, JIA W, et al. Discriminant sparse neighborhood preserving embedding for face recognition [J]. Pattern Recognition, 2012, 45(8):2884-2893.
[17]HOU S, SUN Q. Sparsity preserving canonical correlation analysis with application in feature fusion[J]. Acta Automatica Sinica,2012,38(4):599-665.(侯书东,孙权森.稀疏保持典型相关分析及在特征融合中的应用[J].自动化学报,2012,38(4):599-665.
[18]LAI J, JIANG X. Modular weighted global sparse representation for robust face recognition [J]. Signal Processing Letters, 2012, 19(9):571-574.

基于稀疏重构的判别分析

Sparsity reconstruction-based discriminant analysis

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	杨鼎康黄帅王顺利翟鹏李一丹张立华. 基于对抗生成网络和网络集成的面部表情识别方法EE-GAN[J]. 计算机应用, 0, (): 0-0.
[2]	秦庭威赵鹏程秦品乐曾建朝柴锐黄永琦. 基于残差注意力机制的点云配准算法[J]. 计算机应用, 0, (): 0-0.
[3]	魏淳武, 赵涓涓, 唐笑先, 强彦. 基于多时期蒸馏网络的随访数据知识提取方法[J]. 计算机应用, 2021, 41(10): 2871-2878.
[4]	李自强, 王正勇, 陈洪刚, 李林怡, 何小海. 基于外观和动作特征双预测模型的视频异常行为检测[J]. 计算机应用, 2021, 41(10): 2997-3003.
[5]	裴仪瑶, 郭会明, 张丹普, 陈文博. 基于定位不确定性的鲁棒3D目标检测方法[J]. 计算机应用, 2021, 41(10): 2979-2984.
[6]	许学斌, 张佳达, 刘伟, 路龙宾, 赵雨晴. 融合空间和通道特征的高精度乳腺癌分类方法[J]. 计算机应用, 2021, 41(10): 3025-3032.
[7]	汪虹余, 张彧, 杨恒, 穆楠. 基于蚁群优化算法的弱光图像显著性目标检测[J]. 计算机应用, 2021, 41(10): 2970-2978.
[8]	张怡孙永荣赵科东李华曾庆化. 空中加油场景下的目标联合检测跟踪算法[J]. 计算机应用, 0, (): 0-0.
[9]	马峻姚震徐翠锋陈寿宏. 基于改进PP-YOLO和Deep-SORT的多无人机实时跟踪算法[J]. 计算机应用, 0, (): 0-0.
[10]	贺怀清闫建青惠康华. 基于深度残差网络的轻量级人脸识别方法[J]. 计算机应用, 0, (): 0-0.
[11]	王贺兵, 张春梅. 基于非对称卷积-压缩激发-次代残差网络的人脸关键点检测[J]. 计算机应用, 2021, 41(9): 2741-2747.
[12]	张笑非, 杨阳, 黄佳进, 钟宁. 基于度中心性的认知特征选择方法[J]. 计算机应用, 2021, 41(9): 2767-2772.
[13]	姬张建, 任兴旺. 带旋转与尺度估计的全卷积孪生网络目标跟踪算法[J]. 计算机应用, 2021, 41(9): 2705-2711.
[14]	闫钧华侯平张寅吕向阳马越王高飞. 基于多尺度多分类器卷积神经网络的混合失真类型判定方法 [J]. 计算机应用, 0, (): 0-0.
[15]	李福海蒋慕蓉杨磊谌俊毅. 基于生成对抗网络的梯度引导太阳斑点图像去模糊方法[J]. 计算机应用, 0, (): 0-0.