Transfer learning builds models for the target domain by leveraging the information from another related source domain, in which the distributions of two domains are usually quite distinct. Real-world data are often characterized by multiple representations known as multi-view features. In the multi-view transfer learning field, existing methods aim to address the following two issues. Firstly, due to the distributional difference between the two domains, the classifier trained on the source domain may underperform on the target domain. Moreover, the lack of data from the target domain generally occurs in the training phase. Secondly, how to fully exploit the relations among multiple features is challenging when such multi-view representations emerge in the source and target domains. In this paper, we propose a new coupling loss and self-used privileged information guided multi-view transfer learning method (MVTL-CP). The first issue is addressed by utilizing the weighted labeled data from the source domain to learn a precise classifier for the target domain. Following the consensus and complementarity principles, we tackle the second issue by making the best use of multiple views. Furthermore, we analyze the consistency between views and the generalization capability of MVTL-CP. Comprehensive experiments confirm the effectiveness of our proposed model.

转移学习通过利用来自另一个相关源域的信息来建立目标域的模型，其中两个域的分布通常非常不同。现实世界中的数据通常以称为“多视图要素”的多种表示来表征。在多视图转移学习领域中，现有方法旨在解决以下两个问题。首先，由于两个域之间的分布差异，在源域上训练的分类器可能在目标域上表现不佳。而且，来自目标域的数据的缺乏通常发生在训练阶段。其次，当这样的多视图表示出现在源域和目标域中时，如何充分利用多个功能之间的关系将面临挑战。在本文中，我们提出了一种新的耦合损失和自用特权信息指导的多视图传递学习方法（MVTL-CP）。通过利用来自源域的加权标记数据来学习目标域的精确分类器，可以解决第一个问题。遵循共识和互补原则，我们通过充分利用多种观点来解决第二个问题。此外，我们分析了视图之间的一致性和MVTL-CP的泛化能力。全面的实验证实了我们提出的模型的有效性。我们通过充分利用多种视图来解决第二个问题。此外，我们分析了视图之间的一致性和MVTL-CP的泛化能力。全面的实验证实了我们提出的模型的有效性。我们通过充分利用多种视图来解决第二个问题。此外，我们分析了视图之间的一致性和MVTL-CP的泛化能力。全面的实验证实了我们提出的模型的有效性。