A Second Order Cone programming Formulation for Classifying Missing Data

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Chiranjib Bhattacharyya, Pannagadatta Shivaswamy, Alex Smola


We propose a convex optimization based strategy to deal with uncertainty in the observations of a classification problem. We assume that instead of a sample (xi, yi) a distribution over (xi, yi) is specified. In particu- lar, we derive a robust formulation when the distribution is given by a normal distribution. It leads to Second Order Cone Programming formu- lation. Our method is applied to the problem of missing data, where it outperforms direct imputation.