Global Inference Preserving Projection for Semi-supervised Discriminant Analysis  

作　　者：谷小婧 孙韶媛 方建安 

机构地区：[1]Key Laboratory of Advanced Control and Optimization for Chemical Process,Ministry of Education,East China University of Science and Technology [2]College of Information Science&Technology,Donghua University

出　　处：《Journal of Donghua University(English Edition)》2012年第2期144-147,共4页东华大学学报（英文版）

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

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.

关 键 词：semi-supervised learning dimensionality reduction manifoM structure 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]