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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.20272 |
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| _version_ | 1866911173770739712 |
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| author | Wang, Bingbing Wang, Jiaqi Tang, Yu |
| author_facet | Wang, Bingbing Wang, Jiaqi Tang, Yu |
| contents | Regression prediction plays a crucial role in practical applications and strongly relies on data annotation. However, due to prohibitive annotation costs or domain-specific constraints, labeled data in the target domain is often scarce, making transfer learning a critical solution by leveraging knowledge from resource-rich source domains. In the practical target scenario, although transfer learning has been widely applied, influential points can significantly distort parameter estimation for the target domain model. This issue is further compounded when influential points are also present in source domains, leading to aggravated performance degradation and posing critical robustness challenges for existing transfer learning frameworks. In this study, we innovatively introduce a transfer learning collaborative optimization (Trans-CO) framework for influential point detection and regression model fitting. Extensive simulation experiments demonstrate that the proposed Trans-CO algorithm outperforms competing methods in terms of model fitting performance and influential point identification accuracy. Furthermore, it achieves superior predictive accuracy on real-world datasets, providing a novel solution for transfer learning in regression with influential points |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20272 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transfer Learning in Regression with Influential Points Wang, Bingbing Wang, Jiaqi Tang, Yu Methodology Regression prediction plays a crucial role in practical applications and strongly relies on data annotation. However, due to prohibitive annotation costs or domain-specific constraints, labeled data in the target domain is often scarce, making transfer learning a critical solution by leveraging knowledge from resource-rich source domains. In the practical target scenario, although transfer learning has been widely applied, influential points can significantly distort parameter estimation for the target domain model. This issue is further compounded when influential points are also present in source domains, leading to aggravated performance degradation and posing critical robustness challenges for existing transfer learning frameworks. In this study, we innovatively introduce a transfer learning collaborative optimization (Trans-CO) framework for influential point detection and regression model fitting. Extensive simulation experiments demonstrate that the proposed Trans-CO algorithm outperforms competing methods in terms of model fitting performance and influential point identification accuracy. Furthermore, it achieves superior predictive accuracy on real-world datasets, providing a novel solution for transfer learning in regression with influential points |
| title | Transfer Learning in Regression with Influential Points |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.20272 |