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Unsupervised Active Learning Based on Hierarchical Graph-Theoretic Clustering

文献类型：期刊论文

作者Hu, Weiming1; Hu, Wei1; Xie, Nianhua1; Maybank, Steve2
刊名IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
出版日期2009-10-01
卷号39期号:5页码:1147-1161
关键词Active learning dominant-set clustering image and video classification network intrusion detection spectral clustering
英文摘要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.
WOS标题词Science & Technology ; Technology
类目[WOS]Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
研究领域[WOS]Automation & Control Systems ; Computer Science
关键词[WOS]INTRUSION DETECTION ; ANOMALY DETECTION ; ALGORITHM ; COMMITTEE ; QUERY
收录类别SCI
语种英语
WOS记录号WOS:000267865400006
源URL[http://ir.ia.ac.cn/handle/173211/3256]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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