Name
Abstract | ||
|---|---|---|
As a state-of-the-art multi-class supervised novelty detection method, supervised novelty detection-support vector machine (SND-SVM) is extended from one-class support vector machine (OC-SVM). It still requires to slove a more time-consuming quadratic programming (QP) whose scale is the number of training samples multiplied by the number of normal classes. In order to speed up SND-SVM learning, we propose a down sampling framework for SND-SVM. First, the learning result of SND-SVM is only decided by minor samples that have non-zero Lagrange multipliers. We point out that the potential samples with non-zero Lagrange multipliers are located in the boundary regions of each class. Second, the samples located in boundary regions can be found by a boundary detector. Therefore, any boundary detector can be incorporated into the proposed down sampling framework for SND-SVM. In this paper, we use a classical boundary detector, local outlier factor (LOF), to illustrate the effective of our down sampling framework for SND-SVM. The experiments, conducted on several benchmark datasets and synthetic datasets, show that it becomes much faster to train SND-SVM after down sampling. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s13042-020-01196-2 | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS |
Keywords | DocType | Volume |
SND-SVM, Critical samples, Boundary detection, Subset selection | Journal | 12 |
Issue | ISSN | Citations |
3 | 1868-8071 | 1 |
PageRank | References | Authors |
0.35 | 36 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yugen Yi | 1 | 92 | 15.25 |
| Yanjiao Shi | 2 | 34 | 3.14 |
| Wenle Wang | 3 | 1 | 0.35 |
| Gang Lei | 4 | 27 | 7.73 |
| Jiangyan Dai | 5 | 14 | 4.19 |
| Hao Zheng | 6 | 1 | 0.35 |