CAJ | 학술논문

现实生活中数据的分布往往是非线性且不平衡的,传统的线性鉴别方法已经很难提取有效的鉴别信息,于是文中将算法扩展到核空间,提出了基于欠采样技术的核化正交平衡类鉴别分析( KOCBD)的方法。该方法在非线性空间中使用核映射,令少样本类为特定类,在剩余样本中构建其近邻样本集,并重新进行平衡类划分,然后提取鉴别特征。为了得到更具鉴别力的特征,进一步去除特征间的冗余信息,文中为相关性大的类之间所获得的鉴别向量加上正交约束。在Coil 20和USPS数据库上的实验结果表明,KOCBD方法能够有效地解决非线性空间的类不平衡问题,识别效果有一定程度的提高。
현실생활중수거적분포왕왕시비선성차불평형적,전통적선성감별방법이경흔난제취유효적감별신식,우시문중장산법확전도핵공간,제출료기우흠채양기술적핵화정교평형류감별분석( KOCBD)적방법。해방법재비선성공간중사용핵영사,령소양본류위특정류,재잉여양본중구건기근린양본집,병중신진행평형류화분,연후제취감별특정。위료득도경구감별력적특정,진일보거제특정간적용여신식,문중위상관성대적류지간소획득적감별향량가상정교약속。재Coil 20화USPS수거고상적실험결과표명,KOCBD방법능구유효지해결비선성공간적류불평형문제,식별효과유일정정도적제고。
In the real world,the distribution of the data is usually nonlinear and uneven. Under this circumstance,it is difficult to extract valid discriminant information by using those traditional discriminant approaches. To solve this problem,propose an approach named Ker-nel Orthogonal Class Balanced Discrimination ( KOCBD) . KOCBD maps the class-imbalanced sample set into kernel space. Then,for a specific class which has fewer samples,KOCBD establishes a nearest sample set in the remaining samples,and then redivides them into some even subsets. At last,KOCBD imposes orthogonal constraint on the extracted discriminant vectors among those high correlated clas-ses to remove redundant information. The experimental results on the Coil 20 and USPS databases demonstrate that the KOCBD approach can effectively solve the class-imbalance problem in nonlinear subspace,and achieve better recognition performance.