Title: Feature uncertainty bounding schemes for large robust nonlinear SVM classifiers,
Abstract: We consider the binary classification problem when data are large and subject
to unknown but bounded uncertainties. We address the problem by formulating the
nonlinear support vector machine training problem with robust optimization. To
do so, we analyze and propose two bounding schemes for uncertainties associated
to random approximate features in low dimensional spaces. The proposed
techniques are based on Random Fourier Features and the Nyström methods. The
resulting formulations can be solved with efficient stochastic approximation
techniques such as stochastic (sub)-gradient, stochastic proximal gradient
techniques or their variants.