Unsupervised Domain Adaptation through high-order tensor matching with discriminative manifold learning. (October 2022) - Tian, Qing; Xu, Heng; Sun, Heyang; Yang, Hong; Zhu, Yanan
Abstract: Unsupervised Domain Adaptation (UDA) seeks to exploit the source domain knowledge to address similar but unsupervised target domain tasks. To perform UDA, most of the existing works typically learn domain-invariant representations or align the distributions across source and target domains through low-order statistical matching, which fail to explore more discriminative and high-order adaptation information. In this work, we proposed a kind of UDA, namely UDA through high-order tensor matching with discriminative manifold learning (UDA-HOTDML), by exploring the discriminative manifold structure jointly with the high-order tensor to more desirably match cross-domain alignment. In UDA-HOTDML, multiple aspects of domain knowledge are exploited to benefit more generalizable UDA by jointly modeling the discriminative structures of target domain, consistence as well as high-order tensor statistical characteristics. Finally, our evaluation results show that the proposed UDA-HOTDML method outperforms many related UDA methods.