Smoothing complex-valued signals on Graphs with Monte-Carlo
Methodology
2023-03-01 v6 Statistics Theory
Statistics Theory
Abstract
We introduce new smoothing estimators for complex signals on graphs, based on a recently studied Determinantal Point Process (DPP). These estimators are built from subsets of edges and nodes drawn according to this DPP, making up trees and unicycles, i.e., connected components containing exactly one cycle. We provide a Julia implementation of these estimators and study their performance when applied to a ranking problem.
Keywords
Cite
@article{arxiv.2210.08014,
title = {Smoothing complex-valued signals on Graphs with Monte-Carlo},
author = {Hugo Jaquard and Michaël Fanuel and Pierre-Olivier Amblard and Rémi Bardenet and Simon Barthelmé and Nicolas Tremblay},
journal= {arXiv preprint arXiv:2210.08014},
year = {2023}
}