Unsupervised Active Learning Based on Hierarchical Graph-Theoretic Clustering

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	Unsupervised Active Learning Based on Hierarchical Graph-Theoretic Clustering
	Hu, Weiming1 ; Hu, Wei 1; Xie, Nianhua 1; Maybank, Steve 2
发表期刊	IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
	2009-10-01
卷号	39 期号:5 页码:1147-1161
文章类型	Article
摘要	Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.
关键词	Active Learning Dominant-set Clustering Image And Video Classification Network Intrusion Detection Spectral Clustering
WOS标题词	Science & Technology ; Technology
关键词[WOS]	INTRUSION DETECTION ; ANOMALY DETECTION ; ALGORITHM ; COMMITTEE ; QUERY
收录类别	SCI
语种	英语
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000267865400006
引用统计	被引频次：31[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/173211/3256
专题	多模态人工智能系统全国重点实验室_视频内容安全
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100080, Peoples R China 2.Univ London, Birkbeck Coll, Sch Comp Sci & Informat Syst, London WC1E 7HX, England
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	Hu, Weiming,Hu, Wei,Xie, Nianhua,et al. Unsupervised Active Learning Based on Hierarchical Graph-Theoretic Clustering[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2009,39(5):1147-1161.
APA	Hu, Weiming,Hu, Wei,Xie, Nianhua,&Maybank, Steve.(2009).Unsupervised Active Learning Based on Hierarchical Graph-Theoretic Clustering.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,39(5),1147-1161.
MLA	Hu, Weiming,et al."Unsupervised Active Learning Based on Hierarchical Graph-Theoretic Clustering".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 39.5(2009):1147-1161.