计算机工程2012，Vol.38Issue(6)：181-183,3.DOI:10.3969/j.issn.1000-3428.2012.06.059
半监督的局部保留投影降维方法
Semi-supervised Locality Preserving Projection Dimensionality Reduction Method
谈锐 1陈秀宏2
作者信息
- 1. 江南大学数字媒体学院,江苏无锡214122
- 2. 江南大学物联网工程学院,江苏无锡214122
- 折叠
摘要
Abstract
Existing algorithms can not effectively use rich labeled and unlabeled sample contains valuable information, which is useful for dimensionality reduction. Aiming at this problem, this paper proposes a novel method called Semi-supervised Locality Preserving Projection (SSLPP). It redefines the between-class similarity and within-class similarity, which is used to maximize the between-class separability and minimizes the within-class separability. In addition, the proposed method preserves the global and locality structure of unlabeled samples. Experimental results in artificial data sets, UCI databases and Olivetti face databases show the usefulness of SSLPP.关键词
数据降维/半监督/局部结构/全局结构/相似度/分离度Key words
data dimensionality reduction/ semi-supervised/ local structure/ global structure/ similarity/ separability分类
信息技术与安全科学引用本文复制引用
谈锐,陈秀宏..半监督的局部保留投影降维方法[J].计算机工程,2012,38(6):181-183,3.
