Robust Classification of noisy data using Second Order Cone Programming approach

Assuming an ellipsoidal model of uncertainty a robust formulation for classifying noisy data is presented. The formulation is a convex optimization problem, in par- ticular it is a instance of Second Order Cone Programming problem. The formulation is derived from a worst case consideration and the robustness properties hold for a large class of distributions. The equivalence of ellipsoidal uncertainty and Gaussian noise models is also discussed. The Generalized Optimal hyperplane is recovered as a special case of the robust formulation. Experiments on real world datasets illustrates the efficacy of the formulation