In logistic regression, it is often desirable to utilize regularization to promote sparse solutions, particularly for problems with a large number of features compared to available labels. In this paper, we present screening rules that safely remove features from logistic regression with $\ell_0-\ell_2$ regularization before solving the problem. The proposed safe screening rules are based on lower bounds from the Fenchel dual of strong conic relaxations of the logistic regression problem. Numerical experiments with real and synthetic data suggest that a high percentage of the features can be effectively and safely removed apriori, leading to substantial speed-up in the computations.

翻译：在后勤回归方面,通常需要利用正规化来促进稀缺的解决方案,特别是针对与现有标签相比具有大量特征的问题。本文提出筛选规则,在解决问题之前安全地消除物流回归的特征,先用$_0-/ell_2美元正规化。拟议的安全筛选规则基于Fenchel两边的较低界限,前者是后勤回归问题的明显松散。用真实和合成数据进行的数字实验表明,高比例的特征可以有效和安全地去除,从而导致计算速度大幅加快。