title: Large-scale linear nonparallel support vector machine solver.
abstract: Twin support vector machines (TWSVMs), as the representative nonparallel hyperplane classifiers, have shown the effectiveness over standard SVMs from some aspects. However, they still have some serious defects restricting their further study and real applications: (1) They have to compute and store the inverse matrices before training, it is intractable for many applications where data appear with a huge number of instances as well as features; (2) TWSVMs lost the sparseness by using a quadratic loss function making the proximal hyperplane close enough to the class itself. This paper proposes a Sparse Linear Nonparallel Support Vector Machine, termed as L1-NPSVM, to deal with large-scale data based on an efficient solver-dual coordinate descent (DCD) method. Both theoretical analysis and experiments indicate that our method is not only suitable for large scale problems, but also performs as good as TWSVMs and SVMs.
authors: Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: tyj@ucas.ac.cn.
authors: Yingjie
authors: Y
authors: Tian
authors: Department of Computer Science and Technology, Xuchang University, Xuchang 461000, China; Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China. Electronic address: pingyuan@bupt.edu.cn.
authors: Yuan
authors: Y
authors: Ping
mesh_terms: D000465:Algorithms; D002965:Classification; D003198:Computer Simulation; D057225:Data Mining; D006801:Humans; D016014:Linear Models; D060388:Support Vector Machine
keywords: Classification; Machine learning; Nonparallel support vector machine; Support vector machines