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    Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm

    Access Status
    Fulltext not available
    Authors
    Wang, M.
    Liu, J.
    Ma, S.
    Liu, Wan-Quan
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Wang, M. and Liu, J. and Ma, S. and Liu, W. 2017. Evaluation of K-SVD embedded with modified l1-norm sparse representation algorithm, pp. 84-93.
    Source Title
    Communications in Computer and Information Science
    DOI
    10.1007/978-981-10-6373-2_9
    ISBN
    9789811063725
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/62949
    Collection
    • Curtin Research Publications
    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.

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