Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini · HAL (Le Centre pour la Communication Scientifique Directe) · 2018
In this paper we propose a boosting based multiview learning algorithm, referred 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.