Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport
This addresses a practical problem in machine learning for scenarios where target data has additional features, but it is incremental as it extends existing optimal transport methods to handle dimensionality mismatches.
The paper tackles unsupervised domain adaptation when the target domain has extra features beyond those in the source domain, by formulating it as an optimal transport problem and deriving a learning bound. It validates the method on simulated and real-world data, showing improved performance over baselines.
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.