Robust polynomial classifier using L1-norm minimization

Authors:
K. Assaleh;T. Shanableh
Affiliations:
Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates;Department of Computer Science and Engineering, American University of Sharjah, Sharjah, United Arab Emirates
Venue:
Applied Intelligence
Year:
2010

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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.