文献类型：期刊文献

英文题名：Supervised optimal locality preserving projection

作者：Wong, W. K.[1];Zhao, H. T.[2]

机构：[1]Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China;[2]E China Univ Sci & Technol, Automat Dept, Shanghai 200237, Peoples R China

年份：2012

卷号：45

期号：1

起止页码：186

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20113814336690);WOS:【SCI-EXPANDED(收录号:WOS:000295760700016)】；

基金：The authors would like to thank the anonymous reviewers and the financial support from the General Research Fund of Research Grants Council of Hong Kong (Project No. 531708) and the National Science Foundation of China (Project Nos. 60705006 and 61072090).

语种：英文

外文关键词：Classification; Feature extraction; Dimensionality reduction; Manifold learning

摘要：In the past few years, the computer vision and pattern recognition community has witnessed a rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among these methods, locality preserving projection (LPP) is one of the most promising feature extraction techniques. Unlike the unsupervised learning scheme of LPP, this paper follows the supervised learning scheme, i.e. it uses both local information and class information to model the similarity of the data. Based on novel similarity, we propose two feature extraction algorithms, supervised optimal locality preserving projection (SOLPP) and normalized Laplacian-based supervised optimal locality preserving projection (NL-SOLPP). Optimal here means that the extracted features via SOLPP (or NL-SOLPP) are statistically uncorrelated and orthogonal. We compare the proposed SOLPP and NL-SOLPP with LPP, orthogonal locality preserving projection (OLPP) and uncorrelated locality preserving projection (ULPP) on publicly available data sets. Experimental results show that the proposed SOLPP and NL-SOLPP achieve much higher recognition accuracy. (C) 2011 Elsevier Ltd. All rights reserved.