A Convergent Solution to Two Dimensional Linear Discriminant Analysis | |
Wei Chen; Kaiqi Huang![]() ![]() | |
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. |
条目包含的文件 | 条目无相关文件。 |
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