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Exact Sampling of Determinantal Point Processes without Eigendecomposition

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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Preprints, Working Papers, ...
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Contributor : Claire Launay <>
Submitted on : Tuesday, October 30, 2018 - 10:32:57 AM
Last modification on : Thursday, July 2, 2020 - 5:17:18 PM
Document(s) archivé(s) le : Thursday, January 31, 2019 - 12:20:32 PM


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  • HAL Id : hal-01710266, version 3
  • ARXIV : 1802.08429


Claire Launay, Bruno Galerne, Agnès Desolneux. Exact Sampling of Determinantal Point Processes without Eigendecomposition. 2018. ⟨hal-01710266v3⟩



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