CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Discriminative Least Squares Regression for Multiclass Classification and Feature Selection
Xiang, Shiming1; Nie, Feiping2; Meng, Gaofeng1; Pan, Chunhong1; Zhang, Changshui3
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2012-11-01
Volume23Issue:11Pages:1738-1754
SubtypeArticle
AbstractThis paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called epsilon-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the epsilon-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L-2,L-1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.
KeywordFeature Selection Least Squares Regression Multiclass Classification Sparse Learning
WOS HeadingsScience & Technology ; Technology
WOS KeywordMUTUAL INFORMATION ; PREDICTION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000310370300006
Citation statistics
Cited Times:146[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7999
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA
3.Tsinghua Univ, State Key Lab Intelligent Technol & Syst, Tsinghua Natl Lab Informat Sci & Technol, Dept Automat, Beijing 100084, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xiang, Shiming,Nie, Feiping,Meng, Gaofeng,et al. Discriminative Least Squares Regression for Multiclass Classification and Feature Selection[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2012,23(11):1738-1754.
APA Xiang, Shiming,Nie, Feiping,Meng, Gaofeng,Pan, Chunhong,&Zhang, Changshui.(2012).Discriminative Least Squares Regression for Multiclass Classification and Feature Selection.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,23(11),1738-1754.
MLA Xiang, Shiming,et al."Discriminative Least Squares Regression for Multiclass Classification and Feature Selection".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 23.11(2012):1738-1754.
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