A Second Order Cone Programming Formulation for Classifying Missing Data
Abstract
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.
Cite
Text
Bhattacharyya et al. "A Second Order Cone Programming Formulation for Classifying Missing Data." Neural Information Processing Systems, 2004.Markdown
[Bhattacharyya et al. "A Second Order Cone Programming Formulation for Classifying Missing Data." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/bhattacharyya2004neurips-second/)BibTeX
@inproceedings{bhattacharyya2004neurips-second,
title = {{A Second Order Cone Programming Formulation for Classifying Missing Data}},
author = {Bhattacharyya, Chiranjib and Shivaswamy, Pannagadatta K. and Smola, Alex J.},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {153-160},
url = {https://mlanthology.org/neurips/2004/bhattacharyya2004neurips-second/}
}