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Multi-view learning is a classification setting in which feature vectors consist of multiple views. The goal in this setting is to find a classifier for some or all of the views. We consider a limiting case of multi-view learning termed surrogate supervision multi-view learning (SSML). In the SSML setting, training data consists of two types: unlabeled two-view data examples and labeled single view...
In multi-view learning, a classifier for different partitions (views) of the feature vector is commonly sought after. We consider the special case of surrogate supervision multi-view learning in which a classifier for one view is sought after, however, no labeled examples are available for that view. Instead, the training set consists of only labeled examples for the other view as well as unlabeled...
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 performs exact class inference, given any...
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 labeled examples are provided for that view. Specifically,...
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