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Estimating sparse regression models in multi-task learning and transfer learning through adaptive penalisation

delete2025-10-01
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PRE
AI
A
Armin Rauschenberger *
P
Petr V. Nazarov
E
Enrico Glaab
DOI：10.1093/bioinformatics/btaf406delete
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摘要

摘要

En 中文
方法 这里，我们提出了一种简单的两阶段程序，用于共享相关的高维预测或分类问题之间的信息。在两个阶段中，我们分别对每个问题执行稀疏回归。虽然第一阶段是在没有先验信息的情况下进行的，但我们将第一阶段的系数作为第二阶段的先验信息。具体而言，我们设计了特征特定和符号特定的自适应权重，以共享不同问题之间的特征选择、效应方向和效应大小。结果 所提出的方法适用于多任务学习以及迁移学习。它提供了稀疏模型（即每个问题具有少量非零系数的模型），这些模型易于解释。通过模拟和应用，我们表明，与现有方法相比，它倾向于选择更少的特征，同时实现相似的预测性能。可用性与实现 该方法的实现可在R包sparselink中获取（https://github.com/rauschenberger/sparselink, https://cran.r-project.org/package=sparselink）。
Keyword:
REGULARIZATION

期刊

Bioinformatics 封面图
Bioinformatics
IF:
5.4
论文数:
1.3K
被引数:
17.9W

机构

L
luxembourg institute of health
学者数:
139
论文数: 57
被引数: 0
U
University of Luxembourg
学者数:
228
论文数: 117
被引数: 6
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