Robust polynomial classifier using L ¹-norm minimization

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.