Conroy Bryan, Sajda Paul
Columbia University New York, NY.
JMLR Workshop Conf Proc. 2012;22:246-254.
Regularized logistic regression is a standard classification method used in statistics and machine learning. Unlike regularized least squares problems such as ridge regression, the parameter estimates cannot be computed in closed-form and instead must be estimated using an iterative technique. This paper addresses the computational problem of regularized logistic regression that is commonly encountered in model selection and classifier statistical significance testing, in which a large number of related logistic regression problems must be solved for. Our proposed approach solves the problems simultaneously through an iterative technique, which also garners computational efficiencies by leveraging the redundancies across the related problems. We demonstrate analytically that our method provides a substantial complexity reduction, which is further validated by our results on real-world datasets.
正则化逻辑回归是统计学和机器学习中使用的一种标准分类方法。与诸如岭回归等正则化最小二乘问题不同，参数估计不能以闭式形式计算，而是必须使用迭代技术进行估计。本文解决了正则化逻辑回归在模型选择和分类器统计显著性检验中常见的计算问题，其中必须求解大量相关的逻辑回归问题。我们提出的方法通过一种迭代技术同时解决这些问题，该技术还通过利用相关问题之间的冗余来提高计算效率。我们通过分析证明了我们的方法显著降低了复杂度，这在我们对真实世界数据集的结果中得到了进一步验证。
