The lasso is a popular method to induce shrinkage and sparsity in the solution vector (coefficients) of regression problems, particularly when there are many predictors relative to the number of observations. Solving the lasso in this high-dimensional setting can, however, be computationally demanding. Fortunately, this demand can be alleviated via the use of screening rules that discard predictors prior to fitting the model, leading to a reduced problem to be solved. In this paper, we present a new screening strategy: look-ahead screening. Our method uses safe screening rules to find a range of penalty values for which a given predictor cannot enter the model, thereby screening predictors along the remainder of the path. In experiments we show that these look-ahead screening rules outperform the active warm-start version of the Gap Safe rules.
翻译:长线条是一种常见的方法,可以诱发回归问题的解析矢量(系数)的缩缩和宽度,特别是当与观测数量相比有许多预测器时。然而,在这种高维环境中解决长线条可能是计算上的要求很高的。幸运的是,可以通过使用筛选规则来缓解这一需求,在安装模型之前弃置预测器,从而减少问题。在本文中,我们提出了一个新的筛选战略:目光筛检。我们的方法使用安全筛选规则来寻找一系列惩罚值,而某个预测器无法输入模型,从而沿这条路径的其余部分筛选预测器。我们在实验中显示,这些长线条比“差距安全”规则的热源版本要强。