Multi-View Learning over Structured and Non-Identical Outputs
This addresses the challenge of leveraging ample unlabeled data in multi-view scenarios for machine learning practitioners, though it appears incremental as it builds on existing multi-view methods.
The paper tackles the problem of limited labeled data in multi-view learning by introducing a new algorithm that uses stochastic agreement between views as regularization, achieving better performance than CoBoosting and two-view Perceptron on various classification tasks.
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.