| A Fast Algorithm of Convex Hull Vertices Selection for Online Classification | |
Ding, Shuguang1; Nie, Xiangli2 ; Qiao, Hong2,3,4; Zhang, Bo4,5,6
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| Source Publication | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 2018-04-01 | |
| Volume | 29Issue:4Pages:792-806 |
| Subtype | Article |
| Abstract | 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. |
| Keyword | Convex Hull Decomposition Kernel Online Classification Projection |
| WOS Headings | Science & Technology ; Technology |
| DOI | 10.1109/TNNLS.2017.2648038 |
| WOS Keyword | SUPPORT ; PERCEPTRON |
| Indexed By | SCI |
| Language | 英语 |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000427859600003 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/19387 |
| Collection | 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 |
| Affiliation | 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 |
| 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: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| TNNLS2017.pdf（3029KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View | |
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