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Robust polynomial classifier using L 1-norm minimization

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Abstract

In this paper we present a robust polynomial classifier based on L 1-norm minimization. We do so by reformulating the classifier training process as a linear programming problem. Due to the inherent insensitivity of the L 1-norm to influential observations, class models obtained via L 1-norm minimization are much more robust than their counterparts obtained by the classical least squares minimization (L 2-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 L 1-norm minimization provides superior recognition rates over L 2-norm minimization when the training data contains influential observations especially if the test dataset is noisy.

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Authors and Affiliations

  1. Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates

    K. Assaleh

  2. Department of Computer Science and Engineering, American University of Sharjah, Sharjah, United Arab Emirates

    T. Shanableh

Authors
  1. K. Assaleh
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  2. T. Shanableh
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Correspondence to K. Assaleh.

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Assaleh, K., Shanableh, T. Robust polynomial classifier using L 1-norm minimization. Appl Intell 33, 330–339 (2010). https://doi.org/10.1007/s10489-009-0169-8

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  • DOI: https://doi.org/10.1007/s10489-009-0169-8

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