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A Convergent Solution to Two Dimensional Linear Discriminant Analysis
Wei Chen; Kaiqi Huang; Tieniu Tan; Dacheng Tao
2009
Conference NameInternational Conference on Image Processing
Source PublicationIEEE International Conference on Image Processing, 2009
Pages4133-4136
Conference Date2009
Conference PlaceCairo, Egypt
AbstractThe 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.
Keyword2dlda   convergence   evolutionary Computation
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12701
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
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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