强大的基于角度的转移学习在高维度中
1Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032, USA.
概括
转移学习提高了使用现有数据的模型性能,特别是对于有限的目标数据集. 我们的新型基于角度转移学习 (angleTL) 方法有效地将知识从源向目标群体转移,即使数据异质.
科学领域:
- 统计遗传学 统计遗传学
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
背景情况:
- 转移学习对于在稀缺的目标数据下改善模型性能至关重要.
- 在具有有限数据和异质源群的高维回归中出现了挑战.
- 现有的方法通常需要个人级别的源数据,这些数据可能无法获得.
研究的目的:
- 开发一种新的转移学习方法,用于使用有限的目标数据进行高维回归.
- 在只有模型参数可用时,应对异质源种群的挑战.
- 提出一种减轻负传输和适应目标信号强度的方法.
主要方法:
- 提出了一种新的基于角度的转移学习 (angleTL) 方法,使用预训练的源模型的参数估计.
- 扩展角度TL,以纳入具有不同相关性的多个源模型.
- 利用高维的非对称分析来理解转移效益.
主要成果:
- AngleTL统一了几种基准方法,并适应目标信号强度.
- 该方法有效地减轻了在存在人口异质性的情况下的负面转移.
- 高维分析证实了angleTL对现有方法的优越性.
结论:
- AngleTL提供了一个有效的解决方案,用于在高维回归中转移学习,使用有限和异质数据.
- 该方法可用于跨生物库转移遗传风险预测模型.
- 利用参数估计可以实现知识转移,而不需要个人级别的源数据.
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