Unified Transfer Learning Models in High-Dimensional Linear Regression
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911767144169472 |
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| author | Liu, Shuo Shuo |
| author_facet | Liu, Shuo Shuo |
| contents | Transfer learning plays a key role in modern data analysis when: (1) the target data are scarce but the source data are sufficient; (2) the distributions of the source and target data are heterogeneous. This paper develops an interpretable unified transfer learning model, termed as UTrans, which can detect both transferable variables and source data. More specifically, we establish the estimation error bounds and prove that our bounds are lower than those with target data only. Besides, we propose a source detection algorithm based on hypothesis testing to exclude the nontransferable data. We evaluate and compare UTrans to the existing algorithms in multiple experiments. It is shown that UTrans attains much lower estimation and prediction errors than the existing methods, while preserving interpretability. We finally apply it to the US intergenerational mobility data and compare our proposed algorithms to the classical machine learning algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_00238 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unified Transfer Learning Models in High-Dimensional Linear Regression Liu, Shuo Shuo Machine Learning Transfer learning plays a key role in modern data analysis when: (1) the target data are scarce but the source data are sufficient; (2) the distributions of the source and target data are heterogeneous. This paper develops an interpretable unified transfer learning model, termed as UTrans, which can detect both transferable variables and source data. More specifically, we establish the estimation error bounds and prove that our bounds are lower than those with target data only. Besides, we propose a source detection algorithm based on hypothesis testing to exclude the nontransferable data. We evaluate and compare UTrans to the existing algorithms in multiple experiments. It is shown that UTrans attains much lower estimation and prediction errors than the existing methods, while preserving interpretability. We finally apply it to the US intergenerational mobility data and compare our proposed algorithms to the classical machine learning algorithms. |
| title | Unified Transfer Learning Models in High-Dimensional Linear Regression |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2307.00238 |