论文标题
在高维
Robust angle-based transfer learning in high dimensions
论文作者
论文摘要
转移学习旨在通过利用来自相关源人群的数据来提高目标模型的性能，这在目标数据不足的情况下特别有用。在本文中，我们研究了如何使用有限的目标数据和在异质源种群中训练的现有回归模型来训练高维脊回归模型的问题。我们考虑一个实际设置，仅访问拟合源模型的参数估计值，而不是单个级源数据。在仅使用一个源模型的设置下，我们提出了一种新型的基于柔性角的传递学习（Angletl）方法，该方法利用源和目标模型参数之间的一致性。我们表明，AngLETL通过构造统一了几种基准方法，包括仅使用目标数据训练的目标模型，拟合源数据上的源模型以及基于距离的传输学习方法，这些模型在基于距离的相似性约束下结合了源参数估计值和目标数据。我们还提供算法，以有效地合并多个源模型，以说明某些源模型可能比其他源模型更有帮助。我们的高维渐近分析提供了有关源模型何时有助于目标模型的解释和见解，并证明了Angletl比其他基准方法的优越性。我们进行了广泛的仿真研究，以验证我们的理论结论，并表明将AngLETL应用于多个生物库中转移现有遗传风险预测模型的可行性。
Transfer learning aims to improve the performance of a target model by leveraging data from related source populations, which is known to be especially helpful in cases with insufficient target data. In this paper, we study the problem of how to train a high-dimensional ridge regression model using limited target data and existing regression models trained in heterogeneous source populations. We consider a practical setting where only the parameter estimates of the fitted source models are accessible, instead of the individual-level source data. Under the setting with only one source model, we propose a novel flexible angle-based transfer learning (angleTL) method, which leverages the concordance between the source and the target model parameters. We show that angleTL unifies several benchmark methods by construction, including the target-only model trained using target data alone, the source model fitted on source data, and distance-based transfer learning method that incorporates the source parameter estimates and the target data under a distance-based similarity constraint. We also provide algorithms to effectively incorporate multiple source models accounting for the fact that some source models may be more helpful than others. Our high-dimensional asymptotic analysis provides interpretations and insights regarding when a source model can be helpful to the target model, and demonstrates the superiority of angleTL over other benchmark methods. We perform extensive simulation studies to validate our theoretical conclusions and show the feasibility of applying angleTL to transfer existing genetic risk prediction models across multiple biobanks.
