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In semi-supervised multi-view learning, the input vector is partitioned into two views and a classifier based on each view is sought after. In such settings, often examples which include the two views and a label are available [1]. In this paper, we are interested in the setting where a classifier for examples from one view is sought after although no(More)
We consider multi-view classification for the challenging scenario where, for some views, there are no labeled training examples. Several discriminative approaches have been recently proposed for special instances of this problem. Here, alternatively, we propose a generative semi-supervised mixture model across all views which, via marginalization, flexibly(More)
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