AVERAGE NEIGHBORHOOD MARGIN MAXIMIZATION PROJECTION WITH SMOOTH REGULARIZATION FOR FACE RECOGNITION

摘要：

Dimensionality reduction is among the keys in many fields, most of the traditional method can be categorized as local or global ones. In this paper, we consider the dimension reduction problem with prior information is available, namely, semi-supervised dimension reduction. A new dimension reduction method that can explore both the labeled and unlabeled information in the dataset is proposed. The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data. Specifically, we aim to learn a discriminant function which is as smooth as possible on the data manifold. The target optimization problem involved can be solved efficiently with eigenvalue decomposition. Experimental results on several datasets demonstrate the effectiveness of our method.

关键词： Dimension Reduction Semi-Supervised Learning Linear Discriminant Analysis

作者: XIAO-MING LIU ZHAO-HUI WANG ZHI-LIN FENG

作者单位: College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081 Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 401-406

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）