Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters

With the tremendous generation of data, there are more and more situations where observations are described by more than one view. This is for example the case with multilingual documents that convey the same information in different languages or images that are naturally described according to different set of features (for example SIFT, HOG, CNN etc). In this paper, we study the related machine learning problem that consists in finding an efficient classification model from different information sources that describe the observations. This topic, called multiview learning Atrey et al. [2010], Sun [2013], has been expanding over the past decade, spurred by the seminal work of Blum and Mitchell on co-training Blum and Mitchell [1998] (with only two views). The aim is to learn a classifier which performs better than classifiers trained over each view separately (called view-specific classifier).