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- W4297267189 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." @default.
- W4297267189 created "2022-09-28" @default.
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- W4297267189 date "2022-10-01" @default.
- W4297267189 modified "2023-10-03" @default.
- W4297267189 title "Unsupervised Domain Adaptation through high-order tensor matching with discriminative manifold learning" @default.
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- W4297267189 doi "https://doi.org/10.1016/j.compeleceng.2022.108375" @default.
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