Exact Sampling of Determinantal Point Processes without Eigendecomposition

02/23/2018

∙

by   Claire Launay, et al.

∙

Université d'Orléans

∙

ens-cachan.fr

∙

Université Paris Descartes

∙

0

∙

share

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.