Uncorrelated Locality Preserving Projections

摘要：

In this paper, we propose a new manifold learning algorithm, called Uncorrelated Locality Preserving Projections, to identify the underlying manifold structure of a data set. ULPP tries to find the subspace that best discriminates different face classes by maximizing the between-class distance, while minimizing the within-class distance. Different from Principal component analysis(PCA)that aims to find a linear mapping which preserves total variance by maximizing the trace of feature variance and locality preserving projections(LPP) that is in favor of preserving the neighborhood structure of the data set. We choose proper dimension of subspace that detects the intrinsic manifold structure for classification tasks. Experiments comparing the proposed algorithm with some other popular algorithms on the JAFFE, AT&T, and Yale databases show that our algorithm consistently outperforms others.

关键词： Feature extraction face recognition subspace methods optimal discriminant vectors

作者: Lin Kezheng Lin Sheng

作者单位: College of Computer Science and Technology Harbin University of Science and Technology Harbin China

会议类型: 国际会议

会议名称: The 11th IEEE International Conference on Communications Systems(第11届电子和电气工程师协会国际通信系统会议)(IEEE ICCS 2008)

会议地点: 广州

会议语种:英文

在线出版日期: 2008-11-19（万方平台首次上网日期，不代表论文的发表时间）