Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm
© 2017, Springer Nature Singapore Pte Ltd. The K-SVD algorithm aims to find an adaptive dictionary for a set of signals by using the sparse representation optimization and constrained singular value decomposition. In this paper, firstly, the original K-SVD algorithm, as well as some sparse represent...
| Main Authors: | , , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2017
|
| Online Access: | http://hdl.handle.net/20.500.11937/62949 |
| _version_ | 1848760953060982784 |
|---|---|
| author | Wang, M. Liu, J. Ma, S. Liu, Wan-Quan |
| author_facet | Wang, M. Liu, J. Ma, S. Liu, Wan-Quan |
| author_sort | Wang, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017, Springer Nature Singapore Pte Ltd. The K-SVD algorithm aims to find an adaptive dictionary for a set of signals by using the sparse representation optimization and constrained singular value decomposition. In this paper, firstly, the original K-SVD algorithm, as well as some sparse representation algorithms including l 0 -norm OMP and l 1 -norm Lasso were reviewed. Secondly, the revised Lasso algorithm was embedded into the K-SVD process and a new different K-SVD algorithms with l 1 -norm Lasso embedded in (RL-K-SVD algrithm) was established. Finally, extensive experiments had been completed on necessary parameters determination, further on the performance compare of recovery error and recognition for the original K-SVD and RL-K-SVD algorithms. The results indicate that within a certain scope of parameter settings, the RL-K-SVD algorithm performs better on image recognition than K-SVD; the time cost for training sample number is lower for RL-K-SVD in case that the sample number is increased to a certain extend. |
| first_indexed | 2025-11-14T10:23:58Z |
| format | Conference Paper |
| id | curtin-20.500.11937-62949 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:23:58Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-629492023-08-02T06:39:10Z Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm Wang, M. Liu, J. Ma, S. Liu, Wan-Quan © 2017, Springer Nature Singapore Pte Ltd. The K-SVD algorithm aims to find an adaptive dictionary for a set of signals by using the sparse representation optimization and constrained singular value decomposition. In this paper, firstly, the original K-SVD algorithm, as well as some sparse representation algorithms including l 0 -norm OMP and l 1 -norm Lasso were reviewed. Secondly, the revised Lasso algorithm was embedded into the K-SVD process and a new different K-SVD algorithms with l 1 -norm Lasso embedded in (RL-K-SVD algrithm) was established. Finally, extensive experiments had been completed on necessary parameters determination, further on the performance compare of recovery error and recognition for the original K-SVD and RL-K-SVD algorithms. The results indicate that within a certain scope of parameter settings, the RL-K-SVD algorithm performs better on image recognition than K-SVD; the time cost for training sample number is lower for RL-K-SVD in case that the sample number is increased to a certain extend. 2017 Conference Paper http://hdl.handle.net/20.500.11937/62949 10.1007/978-981-10-6373-2_9 restricted |
| spellingShingle | Wang, M. Liu, J. Ma, S. Liu, Wan-Quan Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm |
| title | Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm |
| title_full | Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm |
| title_fullStr | Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm |
| title_full_unstemmed | Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm |
| title_short | Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm |
| title_sort | evaluation of k-svd embedded with modified l1-norm sparse representation algorithm |
| url | http://hdl.handle.net/20.500.11937/62949 |