首页 | 本学科首页   官方微博 | 高级检索  
     

一种引入成对代价的子类判别分析
引用本文:万建武,杨明. 一种引入成对代价的子类判别分析[J]. 软件学报, 2013, 24(11): 2597-2609
作者姓名:万建武  杨明
作者单位:南京师范大学 计算机科学与技术学院, 江苏 南京 210023;常州大学 信息科学与工程学院, 江苏 常州 213164;南京师范大学 计算机科学与技术学院, 江苏 南京 210023
基金项目:国家自然科学基金(60873176,61272222,61003116);江苏省自然科学基金(BK2011782,BK2011005);江苏省创新基金(CXZZ12_0386)
摘    要:传统的降维方法追求较低的识别错误率,假设不同错分的代价相同,这个假设在一些实际应用中往往不成立.例如,在基于人脸识别的门禁系统中,存在入侵者类和合法者类,将入侵者错分成合法者的损失往往高于将合法者错分成入侵者的损失,而将合法者错分成入侵者的损失又大于将合法者错分成其他合法者的损失.为此,首先通过对人脸识别门禁系统进行分析,将其归为一个代价敏感的子类学习问题,然后将错分代价以及子类信息同时注入判别分析的框架中,提出一种近似于成对贝叶斯风险准则的降维算法.在人脸数据集Extended Yale B以及ORL上的实验结果表明了该算法的有效性.

关 键 词:代价敏感降维  人脸识别  子类学习
收稿时间:2013-04-18
修稿时间:2013-08-02

Pairwise Costs in Subclass Discriminant Analysis
WAN Jian-Wu and YANG Ming. Pairwise Costs in Subclass Discriminant Analysis[J]. Journal of Software, 2013, 24(11): 2597-2609
Authors:WAN Jian-Wu and YANG Ming
Affiliation:School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China;School of Information Science and Engineering, Changzhou University, Changzhou 213164, China;School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
Abstract:Conventional dimensionality reduction algorithms aim to attain low recognition errors, assuming the same misclassification loss from different misclassifications. In some real-world applications, however, this assumption may not hold. For example, in the doorlocker syetem based on face recognition, there are impostor and gallery person. The loss of misclassifying an impostor as a gallery person is larger than misclassifying a gallery person as an impostor, while the loss of misclassifying a gallery person as an impostor can be larger than misclassifying a gallery person as other gallery persons. This paper recognizes the door-locker system based on face recognition as a cost-sensitive subclass learning problem, incorporates the subclass information and misclassification costs into the framework of discriminant analysis at the same time, and proposes a dimensionality reduction algorithm approximate to the pairwise Bayes risk. The experimental results on face datasets Extended Yale B and ORL demonstrate the superiority of the proposed algorithm.
Keywords:cost-sensitive dimensionality reduction  face recognition  subclass learning
点击此处可从《软件学报》浏览原始摘要信息
点击此处可从《软件学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号