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Article: Dictionary learning-based subspace structure identification in spectral clustering

TitleDictionary learning-based subspace structure identification in spectral clustering
Authors
Keywordshigh-dimensional data
Dictionary learning (DL)
nonnegative data
proximal optimization
sparsity
spectral clustering (SC)
subspace structure
Issue Date2013
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2013, v. 24, n. 8, p. 1188-1199 How to Cite?
AbstractIn this paper, we study dictionary learning (DL) approach to identify the representation of low-dimensional subspaces from high-dimensional and nonnegative data. Such representation can be used to provide an affinity matrix among different subspaces for data clustering. The main contribution of this paper is to consider both nonnegativity and sparsity constraints together in DL such that data can be represented effectively by nonnegative and sparse coding coefficients and nonnegative dictionary bases. In the algorithm, we employ the proximal point technique for the resulting DL and sparsity optimization problem. We make use of coding coefficients to perform spectral clustering (SC) for data partitioning. Extensive experiments on real-world high-dimensional and nonnegative data sets, including text, microarray, and image data demonstrate that the proposed method can discover their subspace structures. Experimental results also show that our algorithm is computationally efficient and effective for obtaining high SC performance and interpreting the clustering results compared with the other testing methods. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/276957
ISSN
2020 Impact Factor: 10.451
2020 SCImago Journal Rankings: 2.882
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJing, Liping-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorZeng, Tieyong-
dc.date.accessioned2019-09-18T08:35:10Z-
dc.date.available2019-09-18T08:35:10Z-
dc.date.issued2013-
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2013, v. 24, n. 8, p. 1188-1199-
dc.identifier.issn2162-237X-
dc.identifier.urihttp://hdl.handle.net/10722/276957-
dc.description.abstractIn this paper, we study dictionary learning (DL) approach to identify the representation of low-dimensional subspaces from high-dimensional and nonnegative data. Such representation can be used to provide an affinity matrix among different subspaces for data clustering. The main contribution of this paper is to consider both nonnegativity and sparsity constraints together in DL such that data can be represented effectively by nonnegative and sparse coding coefficients and nonnegative dictionary bases. In the algorithm, we employ the proximal point technique for the resulting DL and sparsity optimization problem. We make use of coding coefficients to perform spectral clustering (SC) for data partitioning. Extensive experiments on real-world high-dimensional and nonnegative data sets, including text, microarray, and image data demonstrate that the proposed method can discover their subspace structures. Experimental results also show that our algorithm is computationally efficient and effective for obtaining high SC performance and interpreting the clustering results compared with the other testing methods. © 2012 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems-
dc.subjecthigh-dimensional data-
dc.subjectDictionary learning (DL)-
dc.subjectnonnegative data-
dc.subjectproximal optimization-
dc.subjectsparsity-
dc.subjectspectral clustering (SC)-
dc.subjectsubspace structure-
dc.titleDictionary learning-based subspace structure identification in spectral clustering-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TNNLS.2013.2253123-
dc.identifier.scopuseid_2-s2.0-84880916591-
dc.identifier.volume24-
dc.identifier.issue8-
dc.identifier.spage1188-
dc.identifier.epage1199-
dc.identifier.eissn2162-2388-
dc.identifier.isiWOS:000322039500002-
dc.identifier.issnl2162-237X-

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