计算机工程与应用2017，Vol.53Issue(5)：147-153,158,8.DOI:10.3778/j.issn.1002-8331.1508-0090
基于成对约束的非线性维数约减框架
General framework for constrained dimensionality reductio n
摘要
Abstract
Semi-supervised dimensionality reduction refers to find the optimal low-dimensional structures from the original high-dimensional data in terms of the joint knowledge from side information and a large number of unlabeled instances. It has been regarded as an effective way to grasp the high-dimensional data such as gene sequence, text data and face images. In this paper, it develops a general framework for semi-supervised dimensionality reduction with pairwise constraints (SSPC). SSPC learns a discriminant adjacent matrix by using pairwise constraints and nearest neighbors of data. Then, it can learn a projection embedding the data from the original space to the low-dimensional space such that intra-cluster instances become even more nearby while extra-cluster instances become as far away from each other as possible. The proposed method can not only find a linear subspace which is optimal for discrimination, but also discover the nonlinear structure of the manifold. Experimental results on various real data sets demonstrate that SSPC is superior to established dimensionality reduction approaches.关键词
维数约简/辅助信息/成对约束/先验隶属度/邻接矩阵Key words
dimensionality reduction/side information/pairwise constraints/prior membership degree/adjacent matrix分类
信息技术与安全科学引用本文复制引用
尹学松,蒋融融,江立飞,施建华..基于成对约束的非线性维数约减框架[J].计算机工程与应用,2017,53(5):147-153,158,8.基金项目
浙江省公益性技术研究应用项目(No.2013C33087) （No.2013C33087）
浙江省高校中青年学科带头人学术攀登项目(No.pd2013446). （No.pd2013446）
