Unsupervised Domain Adaptation through high-order tensor matching with discriminative manifold learning. (October 2022)

• Record Type: Journal Article
• Authors: Tian, Qing; Xu, Heng; Sun, Heyang; Yang, Hong; Zhu, Yanan
• Is Part Of: Computers & electrical engineering. Volume 103(2022)
• Publication Date: 2022-10
• Subjects: Unsupervised Domain Adaptation (UDA) -- Manifolds -- Discriminability -- High-order tensor matching; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108375
• Languages: English
• ISSNs: 0045-7906

Abstract

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.