文献类型：期刊文献

英文题名：Large-scale linear nonparallel SVMs

作者：Liu, Dalian[1,2];Li, Dewei[3,4];Shi, Yong[1,4,5,6];Tian, Yingjie[4,5]

第一作者：Liu, Dalian

通讯作者：Shi, Y[1];Shi, Y[2];Shi, Y[3];Shi, Y[4]

机构：[1]Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China;[2]Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China;[3]Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China;[4]Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[5]Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China;[6]Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA

第一机构：Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China

通讯机构：[1]corresponding author), Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China;[2]corresponding author), Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[3]corresponding author), Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China;[4]corresponding author), Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA.

年份：2018

卷号：22

期号：6

起止页码：1945-1957

外文期刊名：SOFT COMPUTING

收录：;EI(收录号：20165203160242);Scopus(收录号：2-s2.0-85006483973);WOS:【SCI-EXPANDED(收录号:WOS:000426761200017)】；

基金：This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 61472390, 11271361, 71331005, 11226089 and 91546201), Major International (Regional) Joint Research Project (No. 71110107026) and the Beijing Natural Science Foundation (No. 1162005).

语种：英文

外文关键词：Large-scale; Nonparallel SVM; Ramp loss function; ADMM

摘要：Large-scale problems have been a very active topic in machine learning area. In the time of big data, it is a challenge and meaningful work to solve such problems. Standard SVM can make linear classification on large-scale problems effectively, with acceptable training time and excellent prediction accuracy. However, nonparallel SVM (NPSVM) and ramp loss nonparallel SVM (RNPSVM) are proposed with better performance than SVM on benchmark datasets. It is motivated to introduce NPSVMs into the area of large-scale issues. In this paper, we propose large-scale linear NPSVMs, solved by the alternating direction method of multipliers (ADMM), to handle large-scale classification problems. ADMM breaks large problems into smaller pieces, avoiding solving intractable problems and leading to higher training speed. The primal problems of NPSVM are convex and differentiable, and they can be managed directly by ADMM. But the objective functions of RNPSVM, composed of convex ones and concave ones, should first be processed by CCCP algorithm and transformed as a series of convex programs. Then, we apply ADMM to solve these programs in every iteration. Experiments of NPSVMs on large-scale problems verify that the algorithms can classify large-scale tasks effectively.