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.

翻译：本文提出了一系列多视角学习框架下域适应的新结果。先前研究中较少关注域适应中多视角信息的融合。为此，我们基于PAC-Bayesian理论提出泛化边界分析，以整合当前被分别处理的两类范式。首先，基于Germain等人的前期工作，我们将多视角学习概念引入域适应场景中的分布距离度量，从而提出一种专门针对多视角域适应场景的新距离度量。随后，我们给出用于评估该散度的PAC-Bayesian边界。最后，我们将这些新边界与先前研究进行了比较分析。