Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connections to the volume sampling distribution and active learning algorithms for linear regression. As consequences, this paper presents new analysis for the ARP algorithm and faster implementations using rejection sampling.

翻译：自适应随机主元选取（ARP）是近期提出的一种用于列子集选择的高效算法。本文通过建立与体积采样分布以及线性回归主动学习算法的联系，重新阐释了ARP算法。基于此，本文提出了对ARP算法的新分析，并利用拒绝采样实现了更快速的算法版本。