MTC: A Fast and Robust Graph-Based Transductive Learning Method

doi:10.1109/TNNLS.2014.2363679

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	MTC: A Fast and Robust Graph-Based Transductive Learning Method
	Zhang, Yan-Ming1 ; Huang, Kaizhu 2; Geng, Guang-Gang 3; Liu, Cheng-Lin1
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2015-09-01
卷号	26 期号:9 页码:1979-1991
文章类型	Article
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.
关键词	Graph-based Method Large-scale Manifold Learning Semisupervised Learning (Ssl) Transductive Learning (Tl)
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TNNLS.2014.2363679
关键词[WOS]	CONSTRUCTION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000360437300011
引用统计	被引频次：21[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8965
专题	多模态人工智能系统全国重点实验室_模式分析与学习
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
第一作者单位	模式识别国家重点实验室
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.

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