Second Order Cone Programming Formulations for Feature Selection

@article{Bhattacharyya2004SecondOC,
  title={Second Order Cone Programming Formulations for Feature Selection},
  author={Chiranjib Bhattacharyya},
  journal={Journal of Machine Learning Research},
  year={2004},
  volume={5},
  pages={1417-1433}
}
This paper addresses the issue of feature selection for line ar classifiers given the moments of the class conditional densities. The problem is posed as finding a minimal set of features such that the resulting classifier has a low misclassification error. U sing a bound on the misclassification error involving the mean and covariance of class conditiona l densities and minimizing an L1 norm as an approximate criterion for feature selection, a second order programming formulation is derived… CONTINUE READING