Unsupervised domain adaptation (UDA) aims to recognize newly emerged patterns in target domains, which may be unlabeled, by leveraging knowledge from patterns learnt from source domains. However, existing UDA models and algorithms still suffer from heterogeneous domains, known as the heterogeneous unsupervised domain adaptation (HeUDA) issue. To address this issue, this paper presents a novel HeUDA model via n-dimensional fuzzy geometry and fuzzy equivalence relations, called F-HeUDA. The n-dimensional fuzzy geometry is used to propose a metric to measure the similarity between features on one domain. Then, based on this metric, shared fuzzy equivalence relations (SFER) are proposed. The SFER can allow two domains to use the same α to get the same number of clustering categories. Through these clustering categories, knowledge from the heterogeneous source domain can be transferred to the unlabeled target domain. Different to existing HeUDA models, the proposed F-HeUDA model does not need that two domains must have the same number of instances. As a result, the proposed model has a better ability to handle the issue of small datasets. Experiments distributed across four real datasets were conducted to validate the proposed model. This testing regime demonstrates that the proposed model outperforms the state-of-the-art models, especially when the target domain has very few instances.

中文翻译：

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通过共享模糊等价关系的无监督异构域适应


无监督域适应（UDA）旨在通过利用从源域学习的模式中的知识来识别目标域中新出现的模式（可能未标记）。然而，现有的 UDA 模型和算法仍然受到异构域的困扰，称为异构无监督域适应（HeUDA）问题。为了解决这个问题，本文通过 n 维模糊几何和模糊等价关系提出了一种新颖的 HeUDA 模型，称为 F-HeUDA。 n 维模糊几何用于提出一种度量来衡量一个域上特征之间的相似性。然后，基于该度量，提出共享模糊等价关系（SFER）。 SFER可以允许两个域使用相同的α来获得相同数量的聚类类别。通过这些聚类类别，来自异构源域的知识可以转移到未标记的目标域。与现有的 HeUDA 模型不同，所提出的 F-HeUDA 模型不需要两个域必须具有相同数量的实例。因此，所提出的模型具有更好的处理小数据集问题的能力。进行分布在四个真实数据集的实验来验证所提出的模型。这种测试制度表明，所提出的模型优于最先进的模型，特别是当目标域的实例很少时。