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ICML
2010
IEEE

Robust Formulations for Handling Uncertainty in Kernel Matrices

10 years 19 days ago
Robust Formulations for Handling Uncertainty in Kernel Matrices
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.
Sahely Bhadra, Sourangshu Bhattacharya, Chiranjib
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Sahely Bhadra, Sourangshu Bhattacharya, Chiranjib Bhattacharyya, Aharon Ben-Tal
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