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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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https://hal.archives-ouvertes.fr/hal-01710266
Contributor : Claire Launay <>
Submitted on : Wednesday, May 16, 2018 - 4:34:35 PM
Last modification on : Thursday, July 2, 2020 - 5:17:18 PM
Document(s) archivé(s) le : Monday, September 24, 2018 - 5:58:27 PM

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  • HAL Id : hal-01710266, version 2
  • ARXIV : 1802.08429

Citation

Claire Launay, Bruno Galerne, Agnès Desolneux. Exact Sampling of Determinantal Point Processes without Eigendecomposition. 2018. ⟨hal-01710266v2⟩

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