This paper introduces the paired lasso: a generalisation of the lasso for paired covariate settings. Our aim is to predict a single response from two high-dimensional covariate sets. We assume a one-to-one correspondence between the covariate sets, with each covariate in one set forming a pair with a covariate in the other set. Paired covariates arise, for example, when two transformations of the same data are available. It is often unknown which of the two covariate sets leads to better predictions, or whether the two covariate sets complement each other. The paired lasso addresses this problem by weighting the covariates to improve the selection from the covariate sets and the covariate pairs. It thereby combines information from both covariate sets and accounts for the paired structure. We tested the paired lasso on more than 2000 classification problems with experimental genomics data, and found that for estimating sparse but predictive models, the paired lasso outperforms the standard and the adaptive lasso. The R package palasso is available from cran.

中文翻译：

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配对协变量的稀疏分类

本文介绍了配对套索：配对协变量设置的套索的一般化。我们的目标是预测来自两个高维协变量集的单个响应。我们假设协变量集之间是一对一的对应关系，其中一组中的每个协变量与另一组中的协变量形成一对。例如，当相同数据的两次转换可用时，就会出现成对的协变量。通常不知道两个协变量中的哪个会导致更好的预测，或者两个协变量是否彼此互补。配对的套索通过加权协变量以改善对协变量集和协变量对的选择来解决此问题。因此，它组合了来自协变量集的信息，并说明了配对结构。我们使用实验基因组学数据对配对的套索进行了2000多个分类问题的测试，发现对于估计稀疏但具有预测性的模型，配对的套索要优于标准套索和自适应套索。R包palasso可从cran获得。