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Robust Formulations for Handling Uncertainty in Kernel Matrices

Bhadra, Sahely and Bhattacharya, Sourangshu and Bhattacharyya, Chiranjib and Ben-tal, Aharon (2010) Robust Formulations for Handling Uncertainty in Kernel Matrices. In: International Conference on Machine Learning (ICML), 2010.

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Official URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=1...

Abstract

We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we derive a formulation which is robust to such noise. The resulting formulation applies when the noise is Gaussian, or has finite support. The formulation in general is non-convex, but in several cases of interest it reduces to a convex program. The problem of uncertainty in kernel matrix is motivated from the real world problem of classifying proteins when the structures are provided with some uncertainty. The formulation derived here naturally incorporates such uncertainty in a principled manner leading to significant improvements over the state of the art. 1.

Item Type: Conference Paper
Additional Information: Copyright of this article belongs to Spie-Int Soc Optical Engineering.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 20 Dec 2011 06:06
Last Modified: 20 Dec 2011 06:06
URI: http://eprints.iisc.ernet.in/id/eprint/39116

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