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Robust Classification of noisy data using Second Order Cone Programming approach

Bhattacharyya, Chiranjib (2004) Robust Classification of noisy data using Second Order Cone Programming approach. In: International Conference on Intelligent Sensing and Information Processing., 4-7 January 2004, Chennai, India, pp. 433-438.

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Abstract

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.

Item Type: Conference Paper
Additional Information: ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 14 Jun 2004
Last Modified: 19 Sep 2010 04:12
URI: http://eprints.iisc.ernet.in/id/eprint/385

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