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Conference Paper: Non-negative low rank and sparse graph for semi-supervised learning

TitleNon-negative low rank and sparse graph for semi-supervised learning
Authors
Issue Date2012
Citation
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, p. 2328-2335 How to Cite?
AbstractConstructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel non-negative low-rank and sparse (NNLRS) graph for semi-supervised learning. The weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative. We demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis. Extensive experiments testify to the significant advantages of NNLRS-graph over graphs obtained through conventional means. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/326903
ISSN
2023 SCImago Journal Rankings: 10.331

 

DC FieldValueLanguage
dc.contributor.authorZhuang, Liansheng-
dc.contributor.authorGao, Haoyuan-
dc.contributor.authorLin, Zhouchen-
dc.contributor.authorMa, Yi-
dc.contributor.authorZhang, Xin-
dc.contributor.authorYu, Nenghai-
dc.date.accessioned2023-03-31T05:27:23Z-
dc.date.available2023-03-31T05:27:23Z-
dc.date.issued2012-
dc.identifier.citationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, p. 2328-2335-
dc.identifier.issn1063-6919-
dc.identifier.urihttp://hdl.handle.net/10722/326903-
dc.description.abstractConstructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel non-negative low-rank and sparse (NNLRS) graph for semi-supervised learning. The weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative. We demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis. Extensive experiments testify to the significant advantages of NNLRS-graph over graphs obtained through conventional means. © 2012 IEEE.-
dc.languageeng-
dc.relation.ispartofProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition-
dc.titleNon-negative low rank and sparse graph for semi-supervised learning-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/CVPR.2012.6247944-
dc.identifier.scopuseid_2-s2.0-84866660023-
dc.identifier.spage2328-
dc.identifier.epage2335-

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