MTC: A Fast and Robust Graph-Based Transductive Learning Method
文献类型：期刊论文
| 作者 | Zhang, Yan-Ming1 ; Huang, Kaizhu2; Geng, Guang-Gang3; Liu, Cheng-Lin1
|
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2015-09-01 |
| 卷号 | 26期号:9页码:1979-1991 |
| 关键词 | Graph-based method large-scale manifold learning semisupervised learning (SSL) transductive learning (TL) |
| 英文摘要 | Despite the great success of graph-based transductive learning methods, most of them have serious problems in scalability and robustness. In this paper, we propose an efficient and robust graph-based transductive classification method, called minimum tree cut (MTC), which is suitable for large-scale data. Motivated from the sparse representation of graph, we approximate a graph by a spanning tree. Exploiting the simple structure, we develop a linear-time algorithm to label the tree such that the cut size of the tree is minimized This significantly improves graph-based methods, which typically have a polynomial time complexity. Moreover, we theoretically and empirically show that the performance of MTC is robust to the graph construction, overcoming another big problem of traditional graph-based methods. Extensive experiments on public data sets and applications on web-spam detection and interactive image segmentation demonstrate our method's advantages in aspect of accuracy, speed, and robustness. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | CONSTRUCTION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000360437300011 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/8965]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Xian Jiaotong Liverpool Univ, Dept Elect & Elect Engn, Suzhou 215123, Peoples R China 3.Chinese Acad Sci, China Internet Network Informat Ctr, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Yan-Ming,Huang, Kaizhu,Geng, Guang-Gang,et al. MTC: A Fast and Robust Graph-Based Transductive Learning Method[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(9):1979-1991. |
| APA | Zhang, Yan-Ming,Huang, Kaizhu,Geng, Guang-Gang,&Liu, Cheng-Lin.(2015).MTC: A Fast and Robust Graph-Based Transductive Learning Method.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(9),1979-1991. |
| MLA | Zhang, Yan-Ming,et al."MTC: A Fast and Robust Graph-Based Transductive Learning Method".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.9(2015):1979-1991. |
入库方式： OAI收割
来源：自动化研究所
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