Exploring and reconstructing latent domains for multi-source domain adaptation
Multi-Source Domain Adaptation (MSDA) is receiving increased focus as a technique for reliably transferring knowledge from several source domains to a specific target domain. Traditional approaches generally operate under the assumption that samples in each source conform to a consistent distributio...
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| Veröffentlicht in: | Signal processing Jg. 238; S. 110145 |
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| Sprache: | Englisch |
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Elsevier B.V
01.01.2026
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| ISSN: | 0165-1684 |
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| Abstract | Multi-Source Domain Adaptation (MSDA) is receiving increased focus as a technique for reliably transferring knowledge from several source domains to a specific target domain. Traditional approaches generally operate under the assumption that samples in each source conform to a consistent distribution. However, real-world conditions often involve samples derived from diverse distributions, as well as potential data imbalance among different domains. Addressing these challenges, we introduce a novel and trustworthy framework, Multi-source Reconstructed Domain Adaptation (MSRDA), designed to enhance adaptation efficacy while maintaining robust performance and reliability across heterogeneous data sources. To start with,we delve into the latent mixed distributions of each source using clustering techniques, followed by the reconstruction of the latent domains following the original distribution. Additionally, we introduce an adaptive weighting mechanism to mitigate data imbalances.In cases where an latent domain consists of only a few samples, the global features are identified and dominate in that particular domain to help avoid overfitting. Moreover, given the difficulties of optimizing clustering while updating the model,we apply ExpectationMaximization (EM) algorithm to iteratively perform domain reconstruction and domain adaptation.Experiments are performed on two public datasets and one real-world datasets, and experimental results demonstrate that our MSRDA can effectively achieve multi-source domain adaptation through re-. constructing source domain with identified latent domains. |
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| AbstractList | Multi-Source Domain Adaptation (MSDA) is receiving increased focus as a technique for reliably transferring knowledge from several source domains to a specific target domain. Traditional approaches generally operate under the assumption that samples in each source conform to a consistent distribution. However, real-world conditions often involve samples derived from diverse distributions, as well as potential data imbalance among different domains. Addressing these challenges, we introduce a novel and trustworthy framework, Multi-source Reconstructed Domain Adaptation (MSRDA), designed to enhance adaptation efficacy while maintaining robust performance and reliability across heterogeneous data sources. To start with,we delve into the latent mixed distributions of each source using clustering techniques, followed by the reconstruction of the latent domains following the original distribution. Additionally, we introduce an adaptive weighting mechanism to mitigate data imbalances.In cases where an latent domain consists of only a few samples, the global features are identified and dominate in that particular domain to help avoid overfitting. Moreover, given the difficulties of optimizing clustering while updating the model,we apply ExpectationMaximization (EM) algorithm to iteratively perform domain reconstruction and domain adaptation.Experiments are performed on two public datasets and one real-world datasets, and experimental results demonstrate that our MSRDA can effectively achieve multi-source domain adaptation through re-. constructing source domain with identified latent domains. |
| ArticleNumber | 110145 |
| Author | Meng, Xiangyu Zhou, Jun Li, Changsheng Zhang, Chengzhe Fu, Chilin Tan, Meijuan Liang, Wanjun Zhang, Xiaolu |
| Author_xml | – sequence: 1 givenname: Wanjun surname: Liang fullname: Liang, Wanjun organization: Beijing Institute of Technology, No 5 Zhongguancun South Street, Haidian District, Beijing,100081, Beijing, PR China – sequence: 2 givenname: Meijuan surname: Tan fullname: Tan, Meijuan organization: Minzu University of China, 27 Zhongguancun South Street, Haidian District, Beijing, 100081, Beijing, PR China – sequence: 3 givenname: Xiangyu surname: Meng fullname: Meng, Xiangyu organization: Northeast Forestry University, No.26 Hexing Road Xiangfang District, Harbin, 150040, Heilongjiang, PR China – sequence: 4 givenname: Chengzhe surname: Zhang fullname: Zhang, Chengzhe organization: Beijing Institute of Technology, No 5 Zhongguancun South Street, Haidian District, Beijing,100081, Beijing, PR China – sequence: 5 givenname: Jun surname: Zhou fullname: Zhou, Jun organization: Ant Group, A Space, No.569 Xixi Road, Hangzhou, 310000 Zhejiang, PR China – sequence: 6 givenname: Chilin surname: Fu fullname: Fu, Chilin organization: Ant Group, A Space, No.569 Xixi Road, Hangzhou, 310000 Zhejiang, PR China – sequence: 7 givenname: Xiaolu surname: Zhang fullname: Zhang, Xiaolu organization: Ant Group, A Space, No.569 Xixi Road, Hangzhou, 310000 Zhejiang, PR China – sequence: 8 givenname: Changsheng surname: Li fullname: Li, Changsheng email: lcs@bit.edu.cn organization: Beijing Institute of Technology, No 5 Zhongguancun South Street, Haidian District, Beijing,100081, Beijing, PR China |
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| Cites_doi | 10.1007/978-3-642-33709-3_50 10.1016/j.eswa.2020.114078 10.1609/aaai.v32i1.11767 10.18653/v1/D19-1018 10.1109/TIE.2018.2868023 10.1007/s11263-015-0816-y 10.1016/j.sigpro.2018.12.005 10.1145/3065386 10.1609/aaai.v33i01.33015989 10.1007/978-3-642-15561-1_16 10.1109/MSP.2012.2205597 10.1609/aaai.v28i1.9136 10.1016/j.knosys.2022.108466 10.1016/j.inffus.2014.12.003 |
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| Copyright | 2025 |
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| Keywords | Multi-Source domain adaptation Expectation-maximization algorithm Latent domain reconstruction |
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| PublicationTitle | Signal processing |
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| Publisher | Elsevier B.V |
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