147 citations · 368 across the 5 of their papers we have counts for
6 papers
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…
How to learn a graph from smooth signals
Vassilis Kalofolias
We propose a framework that learns the graph structure underlying a set of smooth signals. Given whose rows reside on the vertices of an unknown graph,…
Fast Robust PCA on Graphs
Nauman Shahid, Nathanael Perraudin, Vassilis Kalofolias +2
Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-li…
Robust Principal Component Analysis on Graphs
Nauman Shahid, Vassilis Kalofolias, Xavier Bresson +2
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well…
GSPBOX: A toolbox for signal processing on graphs
Nathanaël Perraudin, Johan Paratte, David Shuman +4
This document introduces the Graph Signal Processing Toolbox (GSPBox) a framework that can be used to tackle graph related problems with a signal processing approach. It explains t…
Matrix Completion on Graphs
Vassilis Kalofolias, Xavier Bresson, Michael Bronstein +1
The problem of finding the missing values of a matrix given a few of its entries, called matrix completion, has gathered a lot of attention in the recent years. Although the proble…