论文标题
使用$ \ ell_0 $ - $ \ ell_2 $正则化的逻辑回归筛选
Safe Screening for Logistic Regression with $\ell_0$-$\ell_2$ Regularization
论文作者
论文摘要
在逻辑回归中，通常需要利用正则化来促进稀疏的解决方案，尤其是对于与可用标签相比，具有大量功能的问题。在本文中，我们提出了筛选规则，该规则在解决问题之前，用$ \ ell_0- \ ell_2 $正则化从逻辑回归中安全删除功能。所提出的安全筛选规则是基于逻辑回归问题的强锥松弛的Fenchel双重双重界限。具有真实和合成数据的数值实验表明，很大一部分的特征可以有效，安全地删除APRIORI，从而在计算中大大加速。
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.
