Issue No. 02 - February (2011 vol. 33)
Şeyda Ertekin , Massachusetts Institute of Technology, Cambridge
Léon Bottou , NEC Labs America, Princeton
C. Lee Giles , The Pennsylvania State University, University Park
In this paper, we propose a nonconvex online Support Vector Machine (SVM) algorithm (LASVM-NC) based on the Ramp Loss, which has the strong ability of suppressing the influence of outliers. Then, again in the online learning setting, we propose an outlier filtering mechanism (LASVM-I) based on approximating nonconvex behavior in convex optimization. These two algorithms are built upon another novel SVM algorithm (LASVM-G) that is capable of generating accurate intermediate models in its iterative steps by leveraging the duality gap. We present experimental results that demonstrate the merit of our frameworks in achieving significant robustness to outliers in noisy data classification where mislabeled training instances are in abundance. Experimental evaluation shows that the proposed approaches yield a more scalable online SVM algorithm with sparser models and less computational running time, both in the training and recognition phases, without sacrificing generalization performance. We also point out the relation between nonconvex optimization and min-margin active learning.
Online learning, nonconvex optimization, support vector machines, active learning.
C. Lee Giles, &. Ertekin and L. Bottou, "Nonconvex Online Support Vector Machines," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 368-381, 2010.