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    Robust Polynomial Classifier Using L1-norm minimization

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    Robust_Polynomial_Classifier.pdf (973.2Kb)
    Date
    2010
    Author
    Assaleh, Khaled
    Shanableh, Tamer
    Advisor(s)
    Unknown advisor
    Type
    Article
    Postprint
    Peer-Reviewed
    Metadata
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    Abstract
    In this paper we present a robust polynomial classifier based on L1-norm minimization. We do so by reformulating the classifier training process as a linear programming problem. Due to the inherent insensitivity of the L1-norm to influential observations, class models obtained via L1-norm minimization are much more robust than their counterparts obtained by the classical least squares minimization (L2-norm). For validation purposes, we apply this method to two recognition problems: character recognition and sign language recognition. Both are examined under different signal to noise ratio (SNR) values of the test data. Results show that L1-norm minimization provides superior recognition rates over L2-norm minimization when the training data contains influential observations especially if the test dataset is noisy.
    DSpace URI
    http://hdl.handle.net/11073/8833
    External URI
    http://dx.doi.org/10.1007/s10489-009-0169-8
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    • Department of Computer Science and Engineering
    • Department of Electrical Engineering

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