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dc.contributor.authorLiu, J.
dc.contributor.authorLiu, Wan-Quan
dc.contributor.authorLi, Q.
dc.contributor.authorMa, S.
dc.contributor.authorChen, G.
dc.date.accessioned2017-01-30T13:27:24Z
dc.date.available2017-01-30T13:27:24Z
dc.date.created2016-12-18T19:31:11Z
dc.date.issued2016
dc.identifier.citationLiu, 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.urihttp://hdl.handle.net/20.500.11937/31792
dc.identifier.doi10.1109/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.publisherIEEE
dc.titleEvaluation of K-SVD with different embedded sparse representation algorithms
dc.typeConference Paper
dcterms.source.startPage426
dcterms.source.endPage432
dcterms.source.title2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
dcterms.source.series2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
dcterms.source.isbn9781509040933
dcterms.source.conference12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available


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