Sparse Subspace Clustering via Group Sparse Coding
We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efificient algorithms for solving...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
SIAM
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/16995 |
| _version_ | 1848749335978704896 |
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| author | Budhaditya, S. Pham, DucSon Phung, D. Venkatesh, S. |
| author2 | Joydeep Ghosh |
| author_facet | Joydeep Ghosh Budhaditya, S. Pham, DucSon Phung, D. Venkatesh, S. |
| author_sort | Budhaditya, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efificient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challenging benchmark datasets ranging from medical record data to image and text clustering and show that they consistently outperform rival methods. |
| first_indexed | 2025-11-14T07:19:19Z |
| format | Conference Paper |
| id | curtin-20.500.11937-16995 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:19:19Z |
| publishDate | 2013 |
| publisher | SIAM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-169952023-02-07T08:01:25Z Sparse Subspace Clustering via Group Sparse Coding Budhaditya, S. Pham, DucSon Phung, D. Venkatesh, S. Joydeep Ghosh Zoran Obradovic data mining sparsity learning sparse subspace clustering optimization regularization We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efificient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challenging benchmark datasets ranging from medical record data to image and text clustering and show that they consistently outperform rival methods. 2013 Conference Paper http://hdl.handle.net/20.500.11937/16995 10.1137/1.9781611972832.15 SIAM fulltext |
| spellingShingle | data mining sparsity learning sparse subspace clustering optimization regularization Budhaditya, S. Pham, DucSon Phung, D. Venkatesh, S. Sparse Subspace Clustering via Group Sparse Coding |
| title | Sparse Subspace Clustering via Group Sparse Coding |
| title_full | Sparse Subspace Clustering via Group Sparse Coding |
| title_fullStr | Sparse Subspace Clustering via Group Sparse Coding |
| title_full_unstemmed | Sparse Subspace Clustering via Group Sparse Coding |
| title_short | Sparse Subspace Clustering via Group Sparse Coding |
| title_sort | sparse subspace clustering via group sparse coding |
| topic | data mining sparsity learning sparse subspace clustering optimization regularization |
| url | http://hdl.handle.net/20.500.11937/16995 |