Determinantal point processes (DPPs) are well known models for diverse subset selection problems, including recommendation tasks, document summarization and image search. In this paper, we discuss a greedy deterministic adaptation of k-DPP. Deterministic algorithms are interesting for many applications, as they provide interpretability to the user by having no failure probability and always returning the same results. First, the ability of the method to yield low-rank approximations of kernel matrices is evaluated by comparing the accuracy of the Nystr\"om approximation on multiple datasets. Afterwards, we demonstrate the usefulness of the model on an image search task.

翻译：确定点过程(DPPs)是不同子集选择问题众所周知的模式,包括建议任务、文件汇总和图像搜索。本文讨论对 k-DPP 进行贪婪的确定性调整。确定性算法对许多应用程序都很有趣,因为它们通过没有失败概率和总是返回相同的结果为用户提供解释性。首先,通过比较多个数据集的Nystr\'om 近似率的准确性,评估产生低级内核基质近似值的方法的能力。随后,我们展示了图像搜索任务模型的有用性。