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    Evaluation of K-SVD with different embedded sparse representation algorithms

    Access Status
    Fulltext not available
    Authors
    Liu, J.
    Liu, Wan-Quan
    Li, Q.
    Ma, S.
    Chen, G.
    Date
    2016
    Type
    Conference Paper
    
    Metadata
    Show full item record
    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.
    Source Title
    2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
    Source Conference
    12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
    DOI
    10.1109/FSKD.2016.7603211
    ISBN
    9781509040933
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/31792
    Collection
    • Curtin Research Publications
    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.

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