Multi-View Learning over Structured and Non-Identical Outputs

TL;DR

This paper introduces a probabilistic multi-view learning algorithm that leverages stochastic agreement between views, improving performance on structured and unstructured classification tasks with limited labeled data.

Contribution

It presents a novel algorithm that generalizes multi-view learning to partial agreement scenarios and minimizes Bhattacharyya distance for full agreement cases.

Findings

Outperforms CoBoosting and two-view Perceptron on several classification problems.

Works effectively on both structured and unstructured data.

Handles partial agreement scenarios seamlessly.

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.