计算机工程与应用2011，Vol.47Issue(20)：181-183,187,4.DOI:10.3778/j.issn.1002-8331.2011.20.051
基于稀疏表示的半监督降维方法
Semi-supervised dimensionality reduction based on sparsity representation
摘要
Abstract
A Semi-Supervised Dimensionality Reduction method based on Sparsity Representation (SpSSDR) is proposed.Un-like other semi-supervised dimensionality reduction methods that construct graphs in steps, SpSSDR simultaneously defines the connectivity and the edges' weights of a graph via sparsity reconstruction coefficients,and then exploits pairwise constraints for dimensionality reduction.Experiments on high dimensional facial data show that SpSSDR is not only robust to noise but also making use of pairwise constraints efficiently.关键词
降维/连接性与权重/稀疏表示/边约束Key words
dimensionality reduction/ connectivity and weights/ sparsity representation/ pairwise constraints分类
信息技术与安全科学引用本文复制引用
张春涛,郭皎,徐家良..基于稀疏表示的半监督降维方法[J].计算机工程与应用,2011,47(20):181-183,187,4.基金项目
重庆市教委科技项目(No.KJ111106). （No.KJ111106）
