@inproceedings{9a1d86b53e004e44a5a241ac7a0d22fb,
title = "A new perspective on convex relaxations of sparse SVM",
abstract = "This paper proposes a convex relaxation of a sparse support vector machine (SVM) based on the perspective relaxation of mixed-integer nonlinear programs. We seek to minimize the zero-norm of the hyperplane normal vector with a standard SVM hinge-loss penalty and extend our approach to a zero-one loss penalty. The relaxation that we propose is a second-order cone formulation that can be efficiently solved by standard conic optimization solvers. We compare the optimization properties and classification performance of the second-order cone formulation with previous sparse SVM formulations suggested in the literature.",
author = "Noam Goldberg and Sven Leyffer and Todd Munsonz",
note = "Publisher Copyright: Copyright {\textcopyright} SIAM.; SIAM International Conference on Data Mining, SDM 2013 ; Conference date: 02-05-2013 Through 04-05-2013",
year = "2013",
month = jan,
day = "1",
doi = "10.1137/1.9781611972832.50",
language = "English",
series = "Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013",
publisher = "Siam Society",
pages = "450--457",
editor = "Joydeep Ghosh and Zoran Obradovic and Jennifer Dy and Zhi-Hua Zhou and Chandrika Kamath and Srinivasan Parthasarathy",
booktitle = "Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013",
}