Evaluation of K-SVD with different embedded sparse representation algorithms
The K-SVD algorithm is a powerful tool in finding an adaptive dictionary for a set of signals via using the sparse representation optimization and constrained singular value decomposition. In this paper, we first review the original K-SVD algorithm as well as some sparse representation algorithms in...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
IEEE
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/31792 |
| _version_ | 1848753481436889088 |
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| author | Liu, J. Liu, Wan-Quan Li, Q. Ma, S. Chen, G. |
| author_facet | Liu, J. Liu, Wan-Quan Li, Q. Ma, S. Chen, G. |
| author_sort | Liu, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The K-SVD algorithm is a powerful tool in finding an adaptive dictionary for a set of signals via using the sparse representation optimization and constrained singular value decomposition. In this paper, we first review the original K-SVD algorithm as well as some sparse representation algorithms including OMP, Lasso and recently proposed IITH. Secondly, we embed the Lasso and IITH sparse representation algorithms into the K-SVD process and establish two new different K-SVD algorithms. Finally, we have done extensive experiments to evaluate the performances of these derived K-SVD algorithms with different pursuit methods and these experiments show that the K-SVD with IITH has distinctive advantages in computational cost and signal recovery performance while the K-SVD with Lasso is not sensitive to initial conditions. |
| first_indexed | 2025-11-14T08:25:12Z |
| format | Conference Paper |
| id | curtin-20.500.11937-31792 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:25:12Z |
| publishDate | 2016 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-317922017-09-13T15:37:43Z Evaluation of K-SVD with different embedded sparse representation algorithms Liu, J. Liu, Wan-Quan Li, Q. Ma, S. Chen, G. The K-SVD algorithm is a powerful tool in finding an adaptive dictionary for a set of signals via using the sparse representation optimization and constrained singular value decomposition. In this paper, we first review the original K-SVD algorithm as well as some sparse representation algorithms including OMP, Lasso and recently proposed IITH. Secondly, we embed the Lasso and IITH sparse representation algorithms into the K-SVD process and establish two new different K-SVD algorithms. Finally, we have done extensive experiments to evaluate the performances of these derived K-SVD algorithms with different pursuit methods and these experiments show that the K-SVD with IITH has distinctive advantages in computational cost and signal recovery performance while the K-SVD with Lasso is not sensitive to initial conditions. 2016 Conference Paper http://hdl.handle.net/20.500.11937/31792 10.1109/FSKD.2016.7603211 IEEE restricted |
| spellingShingle | Liu, J. Liu, Wan-Quan Li, Q. Ma, S. Chen, G. Evaluation of K-SVD with different embedded sparse representation algorithms |
| title | Evaluation of K-SVD with different embedded sparse representation algorithms |
| title_full | Evaluation of K-SVD with different embedded sparse representation algorithms |
| title_fullStr | Evaluation of K-SVD with different embedded sparse representation algorithms |
| title_full_unstemmed | Evaluation of K-SVD with different embedded sparse representation algorithms |
| title_short | Evaluation of K-SVD with different embedded sparse representation algorithms |
| title_sort | evaluation of k-svd with different embedded sparse representation algorithms |
| url | http://hdl.handle.net/20.500.11937/31792 |