Unsupervised active learning based on hierarchical graph-theoretic clustering
Hu, W. and Hu, W. and Xie, N. and Maybank, Stephen J. (2009) Unsupervised active learning based on hierarchical graph-theoretic clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39 (5), pp. 1147-1161. ISSN 1083-4419.
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.
|School:||Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems|
|Date Deposited:||02 Feb 2011 12:03|
|Last Modified:||17 Apr 2013 12:18|
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