| A Convergent Solution to Two Dimensional Linear Discriminant Analysis | |
Wei Chen; Kaiqi Huang ; Tieniu Tan; Dacheng Tao
| |
| 2009 | |
| Conference Name | International Conference on Image Processing |
| Source Publication | IEEE International Conference on Image Processing, 2009 |
| Pages | 4133-4136 |
| Conference Date | 2009 |
| Conference Place | Cairo, Egypt |
| Abstract | 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. |
| Keyword | 2dlda convergence evolutionary Computation |
| Language | 英语 |
| Document Type | 会议论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/12701 |
| Collection | 智能感知与计算研究中心 |
| Corresponding Author | Kaiqi 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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