Towards Deterministic Diverse Subset Sampling

• URL: http://arxiv.org/abs/2105.13942v1
• Date: Fri, 28 May 2021 16:05:58 GMT
• Title: Towards Deterministic Diverse Subset Sampling
• Authors: Joachim Schreurs, Micha\"el Fanuel and Johan A.K. Suykens
• Abstract summary: In this paper, we discuss a greedy deterministic adaptation of k-DPP. We demonstrate the usefulness of the model on an image search task.
• Score: 14.236193187116049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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