Local Reconstruction and Dissimilarity Preserving Semi-Supervised Dimensionality Reduction
作者单位：Department of AutomationCollege of Information and EngineeringYangzhou University
会议名称：《2013年中国智能自动化学术会议》
会议日期：2013年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：the National Science Foundation of China under Grant No.61175111 the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No.10KJB510027 for supporting this work
关 键 词：Local reconstruction Dissimilarity preserving Semi-supervised dimensionality reduction Face recognition
摘 要：In this paper,a semi-supervised dimensionality reduction algorithm for feature extraction,named LRDPSSDR,is proposed by combining local reconstruction with dissimilarity *** focuses on local and global structure based on labeled and unlabeled samples in learning *** sets the edge weights of adjacency graph by minimizing the local reconstruction error and preserves local geometric structure of ***,the dissimilarity between samples is represented by maximizing global scatter matrix so that the global manifold structure can be preserved *** comparison and extensive experiments demonstrate the effectiveness of LRDPSSDR.