Evaluation of K-SVD with different embedded sparse representation algorithms
dc.contributor.author | Liu, J. | |
dc.contributor.author | Liu, Wan-Quan | |
dc.contributor.author | Li, Q. | |
dc.contributor.author | Ma, S. | |
dc.contributor.author | Chen, G. | |
dc.date.accessioned | 2017-01-30T13:27:24Z | |
dc.date.available | 2017-01-30T13:27:24Z | |
dc.date.created | 2016-12-18T19:31:11Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Liu, J. and Liu, W. and Li, Q. and Ma, S. and Chen, G. 2016. Evaluation of K-SVD with different embedded sparse representation algorithms, in Proceedings of the 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 13-15 Aug 2016, pp. 426-432. Changsha: IEEE. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/31792 | |
dc.identifier.doi | 10.1109/FSKD.2016.7603211 | |
dc.description.abstract |
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. | |
dc.publisher | IEEE | |
dc.title | Evaluation of K-SVD with different embedded sparse representation algorithms | |
dc.type | Conference Paper | |
dcterms.source.startPage | 426 | |
dcterms.source.endPage | 432 | |
dcterms.source.title | 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016 | |
dcterms.source.series | 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016 | |
dcterms.source.isbn | 9781509040933 | |
dcterms.source.conference | 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) | |
curtin.department | Department of Computing | |
curtin.accessStatus | Fulltext not available |
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