181 citations · 661 across the 28 of their papers we have counts for
8 papers · 1 filter
Stationary time-vertex signal processing
Andreas Loukas, Nathanaël Perraudin
This paper considers regression tasks involving high-dimensional multivariate processes whose structure is dependent on some {known} graph topology. We put forth a new definition o…
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…
Inpainting of long audio segments with similarity graphs
Nathanael Perraudin, Nicki Holighaus, Piotr Majdak +1
We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that enc…
Towards stationary time-vertex signal processing
Nathanael Perraudin, Andreas Loukas, Francesco Grassi +1
Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor netw…
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud
Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on sta…
Low-Rank Matrices on Graphs: Generalized Recovery & Applications
Nauman Shahid, Nathanael Perraudin, Pierre Vandergheynst
Many real world datasets subsume a linear or non-linear low-rank structure in a very low-dimensional space. Unfortunately, one often has very little or no information about the geo…
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…
Compressive PCA for Low-Rank Matrices on Graphs
Nauman Shahid, Nathanael Perraudin, Gilles Puy +1
We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements. The rows and columns of such matrices belong…