一种引入成对代价的子类判别分析

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.

Pairwise Costs in Subclass Discriminant Analysis

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.