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Article: Label Information Guided Graph Construction for Semi-Supervised Learning

TitleLabel Information Guided Graph Construction for Semi-Supervised Learning
Authors
KeywordsLabel information
low-rank representation
semi-supervised graph learning
Issue Date2017
Citation
IEEE Transactions on Image Processing, 2017, v. 26, n. 9, p. 4182-4192 How to Cite?
AbstractIn the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.
Persistent Identifierhttp://hdl.handle.net/10722/327155
ISSN
2023 Impact Factor: 10.8
2023 SCImago Journal Rankings: 3.556
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhuang, Liansheng-
dc.contributor.authorZhou, Zihan-
dc.contributor.authorGao, Shenghua-
dc.contributor.authorYin, Jingwen-
dc.contributor.authorLin, Zhouchen-
dc.contributor.authorMa, Yi-
dc.date.accessioned2023-03-31T05:29:22Z-
dc.date.available2023-03-31T05:29:22Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Image Processing, 2017, v. 26, n. 9, p. 4182-4192-
dc.identifier.issn1057-7149-
dc.identifier.urihttp://hdl.handle.net/10722/327155-
dc.description.abstractIn the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Image Processing-
dc.subjectLabel information-
dc.subjectlow-rank representation-
dc.subjectsemi-supervised graph learning-
dc.titleLabel Information Guided Graph Construction for Semi-Supervised Learning-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIP.2017.2703120-
dc.identifier.pmid28541200-
dc.identifier.scopuseid_2-s2.0-85028436469-
dc.identifier.volume26-
dc.identifier.issue9-
dc.identifier.spage4182-
dc.identifier.epage4192-
dc.identifier.isiWOS:000404288000007-

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