期刊
OPTIMIZATION LETTERS
卷 14, 期 1, 页码 19-36出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11590-019-01482-1
关键词
Sparse optimization; Cardinality constraint; k-norm; Support vector machine; DC optimization
We treat the feature selection problem in the support vector machine (SVM) framework by adopting an optimization model based on use of the 0 pseudo-norm. The objective is to control the number of non-zero components of the normal vector to the separating hyperplane, while maintaining satisfactory classification accuracy. In our model the polyhedral norm parallel to.parallel to [k], intermediate between parallel to.parallel to 1 and parallel to.parallel to 8, plays a significant role, allowing us to come out with a DC (difference of convex) optimization problem that is tackled by means of DCA algorithm. The results of several numerical experiments on benchmark classification datasets are reported.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据