LSSLP - Local structure sensitive label propagation
文献类型：期刊论文
| 作者 | Zhu, Zhenfeng1,2; Cheng, Jian3 ; Zhao, Yao1,2; Ye, Jieping4
|
| 刊名 | INFORMATION SCIENCES
 |
| 出版日期 | 2016-03-01 |
| 卷号 | 332页码:19-32 |
| 关键词 | Machine learning Semi-supervised learning Label propagation Pattern classification Graph model |
| 英文摘要 | Label 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Information Systems |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | CLASSIFICATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000367106800002 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10650]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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