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LSSLP - Local structure sensitive label propagation
Zhu, Zhenfeng1,2; Cheng, Jian3; Zhao, Yao1,2; Ye, Jieping4
AbstractLabel propagation is an approach to iteratively spread the prior state of label confidence associated with each of samples to its neighbors until achieving a global convergence state. Such process has been shown to hold close connection with a general graph-based regularization framework. Within this framework, a closed- form linear system can be built to carry out label propagation. In this paper, to address several issues inherent with previous graph-based label propagation framework, we propose a reformulated one, i.e., local structure sensitive label propagation (LSSLP). By associating each graph vertex with a local structure sensitive tuning factor, the empirical loss error on each vertex can be controlled preferably to keep consistent with the commonly preconditioned 'cluster assumption' of data structure. Out of consideration for information conservation, we relax the state conservation constraint of label confidence from labeled samples proposed by Belkin etal. (2004) to a more general form. Meanwhile, an inverse-warping procedure is incorporated into the proposed local structure sensitive label propagation framework to maintain large and stable enough classification margin. Based on the felicitous inversion technique for blocked matrix, we extend LSSLP to its incremental and inductive versions and also present computationally efficient implementation of it. Experimental results demonstrate the performance of the reformulated regularization framework for label propagation is much competitive. (C) 2015 Elsevier Inc. All rights reserved.
KeywordMachine Learning Semi-supervised Learning Label Propagation Pattern Classification Graph Model
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Basic Research Program of China(2012CB316400) ; National Natural Science Foundation of China(61172129 ; Program for Changjiang Scholars and Innovative Research Team in University(IRT201206) ; Program for New Century Excellent Talents in University(13-0661) ; Fundamental Research Funds for the Central Universities(2015JBM039) ; 61532005 ; 61572068)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000367106800002
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
2.Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA
Recommended Citation
GB/T 7714
Zhu, Zhenfeng,Cheng, Jian,Zhao, Yao,et al. LSSLP - Local structure sensitive label propagation[J]. INFORMATION SCIENCES,2016,332:19-32.
APA Zhu, Zhenfeng,Cheng, Jian,Zhao, Yao,&Ye, Jieping.(2016).LSSLP - Local structure sensitive label propagation.INFORMATION SCIENCES,332,19-32.
MLA Zhu, Zhenfeng,et al."LSSLP - Local structure sensitive label propagation".INFORMATION SCIENCES 332(2016):19-32.
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