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MTC: A Fast and Robust Graph-Based Transductive Learning Method
Zhang, Yan-Ming1; Huang, Kaizhu2; Geng, Guang-Gang3; Liu, Cheng-Lin1
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2015-09-01
Volume26Issue:9Pages:1979-1991
SubtypeArticle
AbstractDespite 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.
KeywordGraph-based Method Large-scale Manifold Learning Semisupervised Learning (Ssl) Transductive Learning (Tl)
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2014.2363679
WOS KeywordCONSTRUCTION
Indexed BySCI
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:000360437300011
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8965
Collection模式识别国家重点实验室_模式分析与学习
Affiliation1.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
Recommended Citation
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.
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