Second Order Cone Programming Approaches for Handling Missing and Uncertain Data

Shivaswamy, Pannagadatta K and Bhattacharyya, Chiranjib and Smola, Alexander J (2006) Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. In: Journal of Machine Learning Research, 7 . pp. 1283-1314.

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Abstract

We propose a novel second order cone programming formulation for designing robust classifiers which can handle uncertainty in observations. Similar formulations are also derived for designing regression functions which are robust to uncertainties in the regression setting. The proposed formulations are independent of the underlying distribution, requiring only the existence of second order moments. These formulations are then specialized to the case of missing values in observations for both classification and regression problems. Experiments show that the proposed formulations outperform imputation.

Item Type:	Journal Article
Publication:	Journal of Machine Learning Research
Publisher:	Journal of Machine Learning Research
Additional Information:	Copyright of this article belongs to Journal of Machine Learning Research.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	30 May 2008
Last Modified:	19 Sep 2010 04:45
URI:	http://eprints.iisc.ac.in/id/eprint/14128

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