Publications by Pierre Vandergheynst

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first

k

eigenvectors of the similarity matrix' Laplacian, whose computati ...

Ieee2016

Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation

Pierre Vandergheynst, Kirell Maël Benzi, Xavier Bresson, Vasilis Kalofolias

This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode ...

Ieee2016

Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Pierre Vandergheynst, Kirell Maël Benzi, Benjamin Ricaud

Graphs are now ubiquitous in almost every field of research. Recently, new research areas devoted to the analysis of graphs and data associated to their vertices have emerged. Focusing on dynamical processes, we propose a fast, robust and scalable framewor ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Compressive PCA for Low-Rank Matrices on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid

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 to the span of the first few eigenvectors of the graphs constructed between ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Localisation et identification spectrale conjointe de sources large bande par parcimonie groupée

Pierre Vandergheynst, Gilles Puy

Les méthodes de localisation de sources acoustiques à l'aide de réseaux de microphones se limitent généralement à l'étude de sources à bande étroite situées en champ lointain. Le travail présenté a pour objectif de réaliser conjointement la localisation et ...

2016

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

Pierre Vandergheynst, Xavier Bresson, Rodrigo Cerqueira Gonzalez Pena

We cast the problem of source localization on graphs as the simultaneous problem of sparse recovery and diffusion ker- nel learning. An l1 regularization term enforces the sparsity constraint while we recover the sources of diffusion from a single snapshot ...

Ieee2016

Compressive Spectral Clustering

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a fea ...

2016

Ultra-low-power ECG front-end design based on compressed sensing

Pierre Vandergheynst, Hossein Mamaghanian

Ultra-low-power design has been a challenging area for design of the sensor front-ends especially in the area of Wireless Body Sensor Nodes (WBSN), where a limited amount of power budget and hardware resources are available. Since introduction of Compresse ...

ACM/IEEE Press2015

Robust Principal Component Analysis on Graphs

Pierre Vandergheynst, Michael Bronstein, Xavier Bresson, Nauman Shahid, Vasilis Kalofolias

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 with respect to the number of data samples. Robust PCA solves the first iss ...

2015

Laplacian Matrix Learning for Smooth Graph Signal Representation

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the emerging field of signal processing on graphs. In this paper, we address the problem of learning graph Laplacians, which is similar to learning graph topologies, such that the input data fo ...

2015

Pierre Vandergheynst

Graph Chatbot

Chat with Graph Search

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation

Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Compressive PCA for Low-Rank Matrices on Graphs

Localisation et identification spectrale conjointe de sources large bande par parcimonie groupée

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

Compressive Spectral Clustering

Ultra-low-power ECG front-end design based on compressed sensing

Robust Principal Component Analysis on Graphs

Laplacian Matrix Learning for Smooth Graph Signal Representation

Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Compressive PCA for Low-Rank Matrices on Graphs

Localisation et identification spectrale conjointe de sources large bande par parcimonie groupée

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

Compressive Spectral Clustering

Ultra-low-power ECG front-end design based on compressed sensing

Robust Principal Component Analysis on Graphs

Laplacian Matrix Learning for Smooth Graph Signal Representation