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Multi-view semi-supervised learning methods try to exploit the combination of multiple views along with large amounts of unlabeled data in order to learn better predictive functions when limited labeled data is available. However, lack of complete view data limits the applicability of multi-view semi-supervised learning to real world data. Commonly, one data view is readily and cheaply available, but additionally views may be costly or only available in some cases. This work aims to make
doi:10.1145/2396761.2398429
dblp:conf/cikm/QuanzH12
fatcat:iwadkmkmlrenteas26df3iaq64