Unsupervised Heterogeneous Domain Adaptation via Shared Fuzzy Equivalence Relations

Abstract

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 $\alpha $ 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.