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- Publisher Website: 10.1109/TNNLS.2013.2253123
- Scopus: eid_2-s2.0-84880916591
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Article: Dictionary learning-based subspace structure identification in spectral clustering
Title | Dictionary learning-based subspace structure identification in spectral clustering |
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Authors | |
Keywords | high-dimensional data Dictionary learning (DL) nonnegative data proximal optimization sparsity spectral clustering (SC) subspace structure |
Issue Date | 2013 |
Citation | IEEE Transactions on Neural Networks and Learning Systems, 2013, v. 24, n. 8, p. 1188-1199 How to Cite? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/276957 |
ISSN | 2023 Impact Factor: 10.2 2023 SCImago Journal Rankings: 4.170 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Jing, Liping | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Zeng, Tieyong | - |
dc.date.accessioned | 2019-09-18T08:35:10Z | - |
dc.date.available | 2019-09-18T08:35:10Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2013, v. 24, n. 8, p. 1188-1199 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | http://hdl.handle.net/10722/276957 | - |
dc.description.abstract | In 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.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.subject | high-dimensional data | - |
dc.subject | Dictionary learning (DL) | - |
dc.subject | nonnegative data | - |
dc.subject | proximal optimization | - |
dc.subject | sparsity | - |
dc.subject | spectral clustering (SC) | - |
dc.subject | subspace structure | - |
dc.title | Dictionary learning-based subspace structure identification in spectral clustering | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TNNLS.2013.2253123 | - |
dc.identifier.scopus | eid_2-s2.0-84880916591 | - |
dc.identifier.volume | 24 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 1188 | - |
dc.identifier.epage | 1199 | - |
dc.identifier.eissn | 2162-2388 | - |
dc.identifier.isi | WOS:000322039500002 | - |
dc.identifier.issnl | 2162-237X | - |