Affiliations: [a] Department of Automation, Shanghai Jiao Tong University, Shanghai, China | [b] Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
Correspondence:
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Corresponding author: Chunjiang Fu, Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China. Tel.: +86 021 34204261; E-mail:[email protected]
Abstract: A new stopping criterion for active learning SVM is proposed. It takes
advantage of the low density separation idea, which is extensively used in
semi-supervised learning and unsupervised learning methods. When the
separating hyperplane of an active learning SVM (ALSVM) lies in a sparse
region in the feature space, its generalization error is expected to have
reached a local minimum, and active learning can stop in such circumstances.
An improved Normalized Cut algorithm, Stochastic Search Normalized Cut
(SSNcut), is employed for measuring whether SVM's separating hyperplane lies
in a low density region. In each active learning iteration, the label vector
predicted by SVM is compared with that obtained from SSNcut. If these two
label vectors are very similar to each other, the SVM's separating
hyperplane can be considered as lying in a sparse region. When calculating
these label vectors, only points that are nearest to the current separating
hyperplane of ALSVM are considered. This makes Normalized Cut less likely to
be influenced by outliers. Experiments show that the proposed stopping
criterion strikes a good balance between generalization performance and
label cost.
Keywords: Active learning, normalized cut, spectral clustering, SVM
