DeepAI AI Chat
Log In Sign Up

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

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

03/27/2018

Distributed Adaptive Sampling for Kernel Matrix Approximation

Most kernel-based methods, such as kernel or Gaussian process regression...
05/26/2016

Kronecker Determinantal Point Processes

Determinantal Point Processes (DPPs) are probabilistic models over all s...
07/10/2019

Barnes-Hut Approximation for Point SetGeodesic Shooting

Geodesic shooting has been successfully applied to diffeo-morphic regist...
07/04/2015

Inference for determinantal point processes without spectral knowledge

Determinantal point processes (DPPs) are point process models that natur...
06/28/2021

Exact simulation of extrinsic stress-release processes

We present a new and straightforward algorithm that simulates exact samp...
09/19/2018

DPPy: Sampling Determinantal Point Processes with Python

Determinantal point processes (DPPs) are specific probability distributi...
07/26/2021

Stable Dynamic Mode Decomposition Algorithm for Noisy Pressure-Sensitive Paint Measurement Data

In this study, we proposed the truncated total least squares dynamic mod...