Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport

TL;DR

This paper introduces an optimal transport-based method for unsupervised domain adaptation when the target domain has additional features not present in the source, focusing on leveraging common features despite differing dimensionalities.

Contribution

It proposes a novel OT-based approach for domain adaptation with extra target features and derives a theoretical learning bound for the method.

Findings

Validated on simulated data

Validated on real-world data

Effective in leveraging common features despite feature dimensionality differences

Abstract

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.