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A Convergent Solution to Two Dimensional Linear Discriminant Analysis
Wei Chen; Kaiqi Huang; Tieniu Tan; Dacheng Tao
2009
会议名称International Conference on Image Processing
会议录名称IEEE International Conference on Image Processing, 2009
页码4133-4136
会议日期2009
会议地点Cairo, Egypt
摘要The matrix based data representation has been recognized to be effective for face recognition because it can deal with the undersampled problem. One of the most popular algorithms, the two dimensional linear discriminant analysis (2DLDA), has been identified to be effective to encode the discriminative information for training matrix represented samples. However, 2DLDA does not converge in the training stage. This paper presents an evolutionary computation based solution, referred to as E-2DLDA, to provide a convergent training stage for 2DLDA. In E-2DLDA, every randomly generated candidate projection matrices are first normalized. The evolutionary computation method optimizes the projection matrices to best separate different classes. Experimental results show E-2DLDA is convergent and outperforms 2DLDA.
关键词2dlda   convergence   evolutionary Computation
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12701
专题智能感知与计算研究中心
通讯作者Kaiqi Huang
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Wei Chen,Kaiqi Huang,Tieniu Tan,et al. A Convergent Solution to Two Dimensional Linear Discriminant Analysis[C],2009:4133-4136.
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