文献类型：期刊文献

英文题名：Robust L2,1-norm distance enhanced multi-weight vector projection support vector machine

作者：Zhao, Henghao[1] Ye, Qiaolin[1] Naiem, Meen Abdullah[1] Fu, Liyong[2]

第一作者：Zhao, Henghao

通信作者：Ye, Qiaolin

机构：[1] College of Information Science and Technology, Nanjing Forestry University, Nanjing, 210094, China; [2] Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China

年份：2019

卷号：7

起止页码：3275-3286

外文期刊名：IEEE Access

收录：EI(收录号：20184606058023)

基金：This work was supported in part by the Central Public-interest Scientific Institution Basal Research Fund under Grant CAFYBB2016SZ003, in part by the National Science Foundations of China under Grant 61871444, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20171453, in part by the National Science Foundation of China under Grant 61773210, and in part by the Jiangsu Key Laboratory for Internet of Things and Mobile Internet Technology.

语种：英文

外文关键词：Iterative methods - Robustness (control systems) - Support vector machines

摘要：The enhanced multi-weight vector projection support vector machine (EMVSVM) is an outstanding algorithm for binary classification, which is proposed recently. However, it measures the distances in an objective function by the squared L2-norm, which exaggerates the effects of outliers or noisy data. In order to alleviate this problem, we propose an effective novel EMVSVM, termed robust EMVSVM based on the L2,1-norm distance (L2,1-EMVSVM). The distances in the objective of our algorithm are measured by the L2,1-norm. Besides, a new powerful iterative algorithm is designed to solve the formulated objective, whose convergence is ensured by theoretical proofs. Finally, the effectiveness and robustness of L2,1-EMVSVM are verified through extensive experiments. ? 2018 IEEE.