CSpace  > 应用数学研究所
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
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
Volume29Issue:4Pages:792-806
AbstractReducing 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.
KeywordConvex hull decomposition kernel online classification projection
DOI10.1109/TNNLS.2017.2648038
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000427859600003
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/29895
Collection应用数学研究所
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ding, Shuguang]'s Articles
[Nie, Xiangli]'s Articles
[Qiao, Hong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ding, Shuguang]'s Articles
[Nie, Xiangli]'s Articles
[Qiao, Hong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ding, Shuguang]'s Articles
[Nie, Xiangli]'s Articles
[Qiao, Hong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.