  This paper presents a series of new results for domain adaptation in the
multi-view learning setting. The incorporation of multiple views in the domain
adaptation was paid little attention in the previous studies. In this way, we
propose an analysis of generalization bounds with Pac-Bayesian theory to
consolidate the two paradigms, which are currently treated separately. Firstly,
building on previous work by Germain et al., we adapt the distance between
distribution proposed by Germain et al. for domain adaptation with the concept
of multi-view learning. Thus, we introduce a novel distance that is tailored
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.
