CAJ | 학술논문

针对传统典型相关分析(Canonical Correlation Analysis,CCA)的图像识别中出现的小样本(Small Sample Size,SSS)问题,提出二维典型相关分析(Two-Dimensional CCA,2DCCA).首先阐述了2DCCA方法的基本原理并给出了类成员关系矩阵的构造方法,推导出了类成员关系协充差矩阵广义逆的解析解.其次,从理论上证明了2DCCA方法对于解决小样本问题的有效性.最后,利用人脸识别实验来测试该方法的性能,实验结果表明,2DCCA方法有效地解决了图像识别中常见的小样本问题,并且能取得较其他几种基于CCA的人脸识别方法更优的识别结果.
침대전통전형상관분석(Canonical Correlation Analysis,CCA)적도상식별중출현적소양본(Small Sample Size,SSS)문제,제출이유전형상관분석(Two-Dimensional CCA,2DCCA).수선천술료2DCCA방법적기본원리병급출료류성원관계구진적구조방법,추도출료류성원관계협충차구진엄의역적해석해.기차,종이론상증명료2DCCA방법대우해결소양본문제적유효성.최후,이용인검식별실험래측시해방법적성능,실험결과표명,2DCCA방법유효지해결료도상식별중상견적소양본문제,병차능취득교기타궤충기우CCA적인검식별방법경우적식별결과.
The traditional Canonical Correlation Analysis(CCA)based image recognition methods always encounter the Small Sample Size(SSS)problem,which is due to the size of sample and less than the dimension of sample.In order to solve this problem,a new supervised learning method called Two-Dimensional CCA(2DCCA)is developed.The theory foundation of 2DCCA method is firsdy developed,and the construction method for the class-membership matrix Y which is used to precisely represent the relationship between samples and classes in the 2DCCA framework is then clarified.Simultaneously,the analytic form of the generalized inverse of such class-membership matrix is derived.From experiment results on face recognition,not only can the SSS problem be effectively solved,but also better recognition performance than several other CCA based methods has been achieved.