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Article: Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features

TitleConstructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features
Authors
KeywordsData Embedding
Graph Construction
Low-Rank and Sparse Representation
Semi-Supervised Learning
Issue Date2015
Citation
IEEE Transactions on Image Processing, 2015, v. 24, n. 11, p. 3717-3728 How to Cite?
AbstractThis paper aims at constructing a good graph to discover the intrinsic data structures under a semisupervised learning setting. First, we propose to build a nonnegative low-rank and sparse (referred to as NNLRS) graph for the given data representation. In particular, the weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse reconstruction coefficients matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph captures both the global mixture of subspaces structure (by the low-rankness) and the locally linear structure (by the sparseness) of the data, hence it is both generative and discriminative. Second, as good features are extremely important for constructing a good graph, we propose to learn the data embedding matrix and construct the graph simultaneously within one framework, which is termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive NNLRS experiments on three publicly available data sets demonstrate that the proposed method outperforms the state-of-the-art graph construction method by a large margin for both semisupervised classification and discriminative analysis, which verifies the effectiveness of our proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/327049
ISSN
2023 Impact Factor: 10.8
2023 SCImago Journal Rankings: 3.556
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhuang, Liansheng-
dc.contributor.authorGao, Shenghua-
dc.contributor.authorTang, Jinhui-
dc.contributor.authorWang, Jingjing-
dc.contributor.authorLin, Zhouchen-
dc.contributor.authorMa, Yi-
dc.contributor.authorYu, Nenghai-
dc.date.accessioned2023-03-31T05:28:26Z-
dc.date.available2023-03-31T05:28:26Z-
dc.date.issued2015-
dc.identifier.citationIEEE Transactions on Image Processing, 2015, v. 24, n. 11, p. 3717-3728-
dc.identifier.issn1057-7149-
dc.identifier.urihttp://hdl.handle.net/10722/327049-
dc.description.abstractThis paper aims at constructing a good graph to discover the intrinsic data structures under a semisupervised learning setting. First, we propose to build a nonnegative low-rank and sparse (referred to as NNLRS) graph for the given data representation. In particular, the weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse reconstruction coefficients matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph captures both the global mixture of subspaces structure (by the low-rankness) and the locally linear structure (by the sparseness) of the data, hence it is both generative and discriminative. Second, as good features are extremely important for constructing a good graph, we propose to learn the data embedding matrix and construct the graph simultaneously within one framework, which is termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive NNLRS experiments on three publicly available data sets demonstrate that the proposed method outperforms the state-of-the-art graph construction method by a large margin for both semisupervised classification and discriminative analysis, which verifies the effectiveness of our proposed method.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Image Processing-
dc.subjectData Embedding-
dc.subjectGraph Construction-
dc.subjectLow-Rank and Sparse Representation-
dc.subjectSemi-Supervised Learning-
dc.titleConstructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIP.2015.2441632-
dc.identifier.scopuseid_2-s2.0-84938528368-
dc.identifier.volume24-
dc.identifier.issue11-
dc.identifier.spage3717-
dc.identifier.epage3728-
dc.identifier.isiWOS:000358615500004-

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