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  • A robust matching pursuit algorithm using information theoretic learning

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    Embargoed until: 2022-05-25
    Author(s)
    Zhang, M
    Gao, Y
    Sun, C
    Blumenstein, M
    Griffith University Author(s)
    Gao, Yongsheng
    Blumenstein, Michael M.
    Zhang, Lena
    Year published
    2020
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    Abstract
    Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are both suboptimal when there are non-Gaussian noises or outliers in the observation data. To overcome these problems, a new OMP algorithm is developed based on information theoretic learning (ITL), which is built on the following new techniques: (1) an ITL-based correlation (ITL-Correlation) is developed as a new similarity measure which can better exploit higher-order statistics of the data, and is robust against many different types of noise and ...
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    Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are both suboptimal when there are non-Gaussian noises or outliers in the observation data. To overcome these problems, a new OMP algorithm is developed based on information theoretic learning (ITL), which is built on the following new techniques: (1) an ITL-based correlation (ITL-Correlation) is developed as a new similarity measure which can better exploit higher-order statistics of the data, and is robust against many different types of noise and outliers in a sparse representation framework; (2) a non-second order statistic measurement and minimization method is developed to improve the robustness of OMP by overcoming the limitation of Gaussianity inherent in a cost function based on second-order moments. The experimental results on both simulated and real-world data consistently demonstrate the superiority of the proposed OMP algorithm in data recovery, image reconstruction, and classification.
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    Journal Title
    Pattern Recognition
    Volume
    107
    DOI
    https://doi.org/10.1016/j.patcog.2020.107415
    Copyright Statement
    © 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Artificial Intelligence and Image Processing
    Information Systems
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/402374
    Collection
    • Journal articles

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