181 citations · 661 across the 28 of their papers we have counts for
3 papers · 1 filter
Large Scale Graph Learning from Smooth Signals
Vassilis Kalofolias, Nathanaël Perraudin
Graphs are a prevalent tool in data science, as they model the inherent structure of the data. They have been used successfully in unsupervised and semi-supervised learning. Typica…
Predicting the evolution of stationary graph signals
Andreas Loukas, Nathanael Perraudin
An emerging way of tackling the dimensionality issues arising in the modeling of a multivariate process is to assume that the inherent data structure can be captured by a graph. Ne…
Global and Local Uncertainty Principles for Signals on Graphs
Nathanael Perraudin, Benjamin Ricaud, David Shuman +1
Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluatin…