PAC-Bayesian Domain Adaptation Bounds for Multi-view learning

This paper presents a series of new results for domain adaptation in the multi-view learning setting. The incorporati on of multiple views in the domain adaptation was paid little attention in t he previous studies. In this way, we propose an analysis of generaliz ation bounds with Pac-Bayesian theory to consolidate the two paradigms, which are currently treated separately. Firstly, building on previo us work by Ger-main et al. [7,8], we adapt the distance between distributio n proposed by Germain et al. for domain adaptation with the concept of mu lti-view learning. Thus, we introduce a novel distance that is ta ilored for the multi-view domain adaptation setting. Then, we give Pac -Bayesian bounds for estimating the introduced divergence. Finally, we compare the different new bounds with the previous studies.