Simultaneous Estimation and Dataset Selection for Transfer Learning in High Dimensions by a Non-convex Penalty
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929587991085056 |
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| author | Li, Zeyu Liu, Dong He, Yong Zhang, Xinsheng |
| author_facet | Li, Zeyu Liu, Dong He, Yong Zhang, Xinsheng |
| contents | In this paper, we propose to estimate model parameters and identify informative source datasets simultaneously for high-dimensional transfer learning problems with the aid of a non-convex penalty, in contrast to the separate useful dataset selection and transfer learning procedures in the existing literature. To numerically solve the non-convex problem with respect to two specific statistical models, namely the sparse linear regression and the generalized low-rank trace regression models, we adopt the difference of convex (DC) programming with the alternating direction method of multipliers (ADMM) procedures. We theoretically justify the proposed algorithm from both statistical and computational perspectives. Extensive numerical results are reported alongside to validate the theoretical assertions. An \texttt{R} package \texttt{MHDTL} is developed to implement the proposed methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2306_04182 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Simultaneous Estimation and Dataset Selection for Transfer Learning in High Dimensions by a Non-convex Penalty Li, Zeyu Liu, Dong He, Yong Zhang, Xinsheng Methodology In this paper, we propose to estimate model parameters and identify informative source datasets simultaneously for high-dimensional transfer learning problems with the aid of a non-convex penalty, in contrast to the separate useful dataset selection and transfer learning procedures in the existing literature. To numerically solve the non-convex problem with respect to two specific statistical models, namely the sparse linear regression and the generalized low-rank trace regression models, we adopt the difference of convex (DC) programming with the alternating direction method of multipliers (ADMM) procedures. We theoretically justify the proposed algorithm from both statistical and computational perspectives. Extensive numerical results are reported alongside to validate the theoretical assertions. An \texttt{R} package \texttt{MHDTL} is developed to implement the proposed methods. |
| title | Simultaneous Estimation and Dataset Selection for Transfer Learning in High Dimensions by a Non-convex Penalty |
| topic | Methodology |
| url | https://arxiv.org/abs/2306.04182 |