Face Recognition Using Marginal Discriminant Linear Local Tangent Space Alignment

摘要：

In this paper, discriminant linear local tangent space alignment (MDLLTSA) is proposed in order to solve the problems of local tangent space alignment (LTSA) in image recognition, such as implicitness of the nonlinear map or class information of data is ignored. With local tangent space representing for local geometrical structure of the manifold of the data samples, as well as with the supervised information for minimizing the margin of the intraclass and maximizing the margin of interclass, we convert the optimization problem of LTSA into multi-object optimization problem to obtain feature extraction space. Compared with classical feature extraction methods, the proposed algorithm obtained stronger classification performance and preserved local geometrical structure as well.

关键词： Local tangent space alignment Marginal discriminant Manifold learning Feature extraction

作者: Yingjing Wang Zhengqun Wang Guoqing Zhang Wei Xu

作者单位: School of Information Engineering, Yangzhou University, Yangzhou 225127, China

会议类型: 国际会议

会议名称: 2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

会议地点: 三亚

会议语种:英文

页码: 1418-1421

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）