Sparsity reconstruction-based discriminant analysis

doi:10.11772/j.issn.1001-9081.2014.06.1608

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (6): 1608-1612.DOI: 10.11772/j.issn.1001-9081.2014.06.1608

• Artificial intelligence • Previous Articles Next Articles

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:2014-07-02 Published:2014-06-01
Contact: QI Mingming

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

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

Abstract

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.

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%。

CLC Number:

TP391.41

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

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.

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Sparsity reconstruction-based discriminant analysis

基于稀疏重构的判别分析

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