Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
This work addresses multiview classification problems, offering an incremental improvement by integrating boosting with PAC-Bayes theory for better generalization.
The paper tackles multiview learning by proposing PB-MVBoost, a boosting algorithm that optimizes weights for view-specific voters and views using a PAC-Bayes multiview C-Bound, and it shows efficiency compared to state-of-the-art models on three datasets.
In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specific classifiers and the diversity between the views. We derive a generalization bound for this strategy following the PAC-Bayes theory which is a suitable tool to deal with models expressed as weighted combination over a set of voters. Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.