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2008

Multi-View Learning over Structured and Non-Identical Outputs

9 years 2 months ago
Multi-View Learning over Structured and Non-Identical Outputs
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 sufficent 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 flat and structured classification problems.
Kuzman Ganchev, João Graça, John Bli
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where UAI
Authors Kuzman Ganchev, João Graça, John Blitzer, Ben Taskar
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