Multiple-View Multiple-Learner Semi-Supervised Learning

摘要

Some recent successful semi-supervised learning methods construct more than one learner from both labeled and unlabeled data for inductive learning. This paper proposes a novel multiple-view multiple-learner (MVML) framework for semi-supervised learning, which differs from previous methods in possession of both multiple views and multiple learners. This method adopts a co-training styled learning paradigm in enlarging labeled data from a much larger set of unlabeled data. To the best of our knowledge it is the first attempt to combine the advantages of multiple-view learning and ensemble learning for semi-supervised learning. The use of multiple views is promising to promote performance compared with single-view learning because information is more effectively exploited. At the same time, as an ensemble of classifiers is learned from each view, predictions with higher accuracies can be obtained than solely adopting one classifier from the same view. Experiments on different applications involving both multiple-view and single-view data sets show encouraging results of the proposed MVML method.