Manifold Coarse Graining for Online Semi-Supervised Learning
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Since labeling data is often both laborious and costly, the labeled data available in many applications is rather limited. Active learning is a learning approach which actively selects unlabeled data points to label as a way to alleviate the labeled data deficiency problem. In this paper, we extend a previous active learning method called transductive experimental design (TED) by proposing a new unlabeled data selection criterion. Our method, called discriminative experimental design (DED), incorporates both margin-based discriminative information and data distribution information and hence it can be seen as a discriminative extension of TED. We report experiments conducted on some benchmark data sets to demonstrate the effectiveness of DED.
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