文献类型：期刊文献

中文题名：Global Inference Preserving Projection for Semi-supervised Discriminant Analysis

英文题名：Global inference preserving projection for semi-supervised discriminant analysis

作者：Gu, Xiao-Jing[1,2]; Sun, Shao-Yuan[2]; Fang, Jian-An[2]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; [2] College of Information Science and Technology, Donghua University, Shanghai 201620, China

年份：2012

卷号：29

期号：2

起止页码：144

中文期刊名：Journal of Donghua University(English Edition)

外文期刊名：Journal of Donghua University (English Edition)

收录：EI(收录号：20123615396231);Scopus

基金：National Natural Science Foundations of China (No.61072090,60874113)

语种：英文

中文关键词：semi-supervised learning; dimensionality reduction; manifoM structure

外文关键词：Supervised learning

摘要：Semi-supervised dimensionality reduction is an important research area for data classification. A new linear dimensionality reduction approach, global inference preserving projection （GIPP）, was proposed to perform classification task in semi-supervised case. GIPP provided a global structure that utilized the underlying discriminative knowledge of unlabeled samples. It used path-based dissimilarity measurement to infer the class label information for unlabeled samples and transformd the diseriminant algorithm into a generalized eigenequation problem. Experimental results demonstrate the effectiveness of the proposed approach.
Semi-supervised dimensionality reduction is an important research area for data classification. A new linear dimensionality reduction approach, global inference preserving projection (GIPP), was proposed to perform classification task in semi-supervised case. GIPP provided a global structure that utilized the underlying discriminative knowledge of unlabeled samples. It used path-based dissimilarity measurement to infer the class label information for unlabeled samples and transformd the discriminant algorithm into a generalized eigenequation problem. Experimental results demonstrate the effectiveness of the proposed approach. Copyright ? 2012 by Editorial Board of Journal of Donghua University, Shanghai, China.