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dc.contributor.authorWang, M.
dc.contributor.authorLiu, J.
dc.contributor.authorMa, S.
dc.contributor.authorLiu, Wan-Quan
dc.date.accessioned2018-02-06T06:14:35Z
dc.date.available2018-02-06T06:14:35Z
dc.date.created2018-02-06T05:49:53Z
dc.date.issued2017
dc.identifier.citationWang, M. and Liu, J. and Ma, S. and Liu, W. 2017. Evaluation of K-SVD embedded with modified l<inf>1</inf>-norm sparse representation algorithm, pp. 84-93.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/62949
dc.identifier.doi10.1007/978-981-10-6373-2_9
dc.description.abstract

© 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.

dc.titleEvaluation of K-SVD embedded with modified l<inf>1</inf>-norm sparse representation algorithm
dc.typeConference Paper
dcterms.source.volume762
dcterms.source.startPage84
dcterms.source.endPage93
dcterms.source.titleCommunications in Computer and Information Science
dcterms.source.seriesCommunications in Computer and Information Science
dcterms.source.isbn9789811063725
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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