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Exact Sampling of Determinantal Point Processes without Eigendecomposition

Authors
  • Launay, Claire
  • Galerne, Bruno
  • Desolneux, Agnès
Publication Date
Oct 30, 2018
Source
HAL-UPMC
Keywords
Language
English
License
Unknown
External links

Abstract

Determinantal point processes (DPPs) enable the modelling of repulsion: they provide diverse sets of points. This repulsion is encoded in a kernel K that we can see as a matrix storing the similarity between points. The usual algorithm to sample DPPs is exact but it uses the spectral decomposition of K, a computation that becomes costly when dealing with a high number of points. Here, we present an alternative exact algorithm that avoids the eigenvalues and the eigenvectors computation and that is, for some applications, faster than the original algorithm.

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