Multi-View Learning over Structured and Non-Identical Outputs
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.
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.
