论文标题
使用最佳传输的无监督域适应目标域中的额外功能
Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport
论文作者
论文摘要
域的适应性旨在将从源域获得的标记实例的知识转移到目标域，以填补域之间的空白。大多数域适应方法都假定源和目标域具有相同的维度。当每个域中的特征数量不同时，适用的方法很少被研究，尤其是当未提供从目标域获得的测试数据的标签信息时。在本文中，假定在两个域中都存在共同特征，并且在目标域中观察到额外的（新的）特征。因此，目标域的维度高于源域的维度。为了利用共同特征的同质性，这些来源和目标域之间的适应性被公正为最佳运输（OT）问题。此外，得出了基于IT的方法的目标域中的学习绑定。使用模拟和现实世界数据对所提出的算法进行验证。
Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of features is different in each domain have rarely been studied, especially when no label information is given for the test data obtained from the target domain. In this paper, it is assumed that common features exist in both domains and that extra (new additional) features are observed in the target domain; hence, the dimensionality of the target domain is higher than that of the source domain. To leverage the homogeneity of the common features, the adaptation between these source and target domains is formulated as an optimal transport (OT) problem. In addition, a learning bound in the target domain for the proposed OT-based method is derived. The proposed algorithm is validated using both simulated and real-world data.
