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Multi-View Learning over Structured and Non-Identical Outputs

Authors
  • Ganchev, Kuzman
  • Graca, Joao
  • Blitzer, John
  • Taskar, Ben
Type
Preprint
Publication Date
Jun 13, 2012
Submission Date
Jun 13, 2012
Identifiers
arXiv ID: 1206.3256
Source
arXiv
License
Yellow
External links

Abstract

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.

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