Lab Papers (GRASP)

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Conference Paper

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Reprinted from:

Kuzman Ganchev, João Graça, John Blitzer, Benjamin Taskar. Multi-View Learning over Structured and Non-Identical Outputs. In UAI 2008, Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, July 9-12, 2008, Helsinki, Finland 2008


In many machine learning problems, labeled training data is limited but unlabeled data is ample. Some of these problems have instances that can be factored into multiple views, each of which is nearly sufficient in determining the correct labels. In this paper we present a new algorithm for probabilistic multi-view learning which uses the idea of stochastic agreement between views as regularization. Our algorithm works on structured and unstructured problems and easily generalizes to partial agreement scenarios. For the full agreement case, our algorithm minimizes the Bhattacharyya distance between the models of each view, and performs better than CoBoosting and two-view Perceptron on several at and structured classification problems.



Date Posted: 07 October 2009