A Fast Algorithm of Convex Hull Vertices Selection for Online Classification

doi:10.1109/TNNLS.2017.2648038

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	A Fast Algorithm of Convex Hull Vertices Selection for Online Classification
	Ding, Shuguang 1; Nie, Xiangli2 ; Qiao, Hong2,3,4 ; Zhang, Bo 4,5,6
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2018-04-01
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
DOI	10.1109/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：23[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	多模态人工智能系统全国重点实验室_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
推荐引用方式 GB/T 7714	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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