A Fast Algorithm of Convex Hull Vertices Selection for Online Classification
Ding, Shuguang1; Nie, Xiangli2; Qiao, Hong2,3,4; Zhang, Bo4,5,6
2018-04-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号29期号:4页码:792-806
文章类型Article
摘要Reducing samples through convex hull vertices selection (CHVS) within each class is an important and effective method for online classification problems, since the classifier can be trained rapidly with the selected samples. However, the process of CHVS is NP-hard. In this paper, we propose a fast algorithm to select the convex hull vertices, based on the convex hull decomposition and the property of projection. In the proposed algorithm, the quadratic minimization problem of computing the distance between a point and a convex hull is converted into a linear equation problem with a low computational complexity. When the data dimension is high, an approximate, instead of exact, convex hull is allowed to be selected by setting an appropriate termination condition in order to delete more nonimportant samples. In addition, the impact of outliers is also considered, and the proposed algorithm is improved by deleting the outliers in the initial procedure. Furthermore, a dimension convention technique via the kernel trick is used to deal with nonlinearly separable problems. An upper bound is theoretically proved for the difference between the support vector machines based on the approximate convex hull vertices selected and all the training samples. Experimental results on both synthetic and real data sets show the effectiveness and validity of the proposed algorithm.
关键词Convex Hull Decomposition Kernel Online Classification Projection
WOS标题词Science & Technology ; Technology
DOI10.1109/TNNLS.2017.2648038
关键词[WOS]SUPPORT ; PERCEPTRON
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000427859600003
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19387
专题复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
作者单位1.Chinese Acad Sci, Inst Appl Math, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management Control Complex Syst, Beijing 100190, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, LSEC, Beijing 100190, Peoples R China
6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
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Ding, Shuguang,Nie, Xiangli,Qiao, Hong,et al. A Fast Algorithm of Convex Hull Vertices Selection for Online Classification[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(4):792-806.
APA Ding, Shuguang,Nie, Xiangli,Qiao, Hong,&Zhang, Bo.(2018).A Fast Algorithm of Convex Hull Vertices Selection for Online Classification.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(4),792-806.
MLA Ding, Shuguang,et al."A Fast Algorithm of Convex Hull Vertices Selection for Online Classification".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.4(2018):792-806.
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