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On surrogate supervision multi-view learning

Data can be represented in multiple views. Traditional multi-view learning methods (i.e., co-training, multi-task learning) focus on improving learning performance using information from the auxiliary view, although information from the target view is sufficient for learning task. However, this work addresses a semi-supervised case of multi-view learning, the surrogate supervision multi-view learning, where labels are available on limited views and a classifier is obtained on the target view where labels are missing. In surrogate multi-view learning, one cannot obtain a classifier without information from the auxiliary view. To solve this challenging problem, we propose discriminative and generative approaches. / Graduation date: 2013
Date03 December 2012
CreatorsJin, Gaole
ContributorsRaich, Raviv
Source SetsOregon State University
Detected LanguageEnglish

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