Approximate Inference in Continuous Determinantal Point Processes

@article{Affandi2013ApproximateII,
  title={Approximate Inference in Continuous Determinantal Point Processes},
  author={Raja Hafiz Affandi and Emily B. Fox and Ben Taskar},
  journal={CoRR},
  year={2013},
  volume={abs/1311.2971}
}
Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion. In machine learning, the focus of DPP-based models has been on diverse subset selection from a discrete and finite base set. This discrete setting admits an efficient sampling algorithm based on the eigendecomposition of the defining kernel matrix. Recently, there has been growing interest in using DPPs defined on continuous spaces. While the discrete-DPP sampler extends formally to the… CONTINUE READING
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