We present novel bounds for coreset construction, feature selection, and dimensionality reduction for logistic regression. All three approaches can be thought of as sketching the logistic regression inputs. On the coreset construction front, we resolve open problems from prior work and present novel bounds for the complexity of coreset construction methods. On the feature selection and dimensionality reduction front, we initiate the study of forward error bounds for logistic regression. Our bounds are tight up to constant factors and our forward error bounds can be extended to Generalized Linear Models.

翻译：我们提出了针对逻辑回归的核心集构建、特征选择与降维的新颖界限。这三种方法均可视为对逻辑回归输入进行素描。在核心集构建方面，我们解决了先前工作中的开放问题，并给出了核心集构建方法复杂度的新界限。在特征选择与降维方面，我们首次研究了逻辑回归的前向误差界限。我们的界限在常数因子范围内是紧致的，且前向误差界限可推广至广义线性模型。