Name
Abstract | ||
|---|---|---|
Support Vector Data Description (SVDD) is a wellknown supervised learning method for novelty detection purpose. For its classification task, SVDD requires a fully-labeled dataset. Nonetheless, contemporary datasets always consist of a collection of labeled data samples jointly a much larger collection of unlabeled ones. This fact impedes the usage of SVDD in the real-world problems. In this paper, we propose to utilize the information implicated in a spectral graph to leverage SVDD in the context of semi-supervised learning. The theory and experiment evidence that the proposed method is able to efficiently employ the information carried in the spectral graph to not only enhance the generalization ability of SVDD but also enforce the cluster assumption which is crucial for a semi-supervised learning method. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | Kernel method, semi-supervised learning, novelty detection, one-class classification, subspace learning |
Field | DocType | ISSN |
Kernel (linear algebra),Graph,Novelty detection,Semi-supervised learning,One-class classification,Pattern recognition,Computer science,Support vector machine,Supervised learning,Artificial intelligence,Kernel method,Machine learning | Conference | 2161-4393 |
Citations | PageRank | References |
4 | 0.40 | 19 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Phuong Duong | 1 | 6 | 1.11 |
| Van Hien Nguyen | 2 | 5 | 1.09 |
| Mi Dinh | 3 | 10 | 1.19 |
| Trung Le | 4 | 31 | 10.29 |
| Dat Tran | 5 | 454 | 78.64 |
| Wanli Ma | 6 | 19 | 8.96 |