  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.
