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Pierre Vandergheynst

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Publications associées (216)

Joint Estimation Of The Room Geometry And Modes With Compressed Sensing

Pierre Vandergheynst, Hervé Lissek, Vu Thach Pham, Helena Peic Tukuljac

Acoustical behavior of a room for a given position of microphone and sound source is usually described using the room impulse response. If we rely on the standard uniform sampling, the estimation of room impulse response for arbitrary positions in the room ...

IEEE2018

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performa ...

Elsevier2018

Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems

Jean-Philippe Thiran, Pierre Vandergheynst, Xavier Bresson, Patric Hagmann, Alessandra Griffa, Alessandro Daducci, Kirell Maël Benzi, Benjamin Ricaud

The study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural ...

Elsevier2017

Locating Temporal Functional Dynamics of Visual Short-Term Memory Binding using Graph Modular Dirichlet Energy

Pierre Vandergheynst, Benjamin Ricaud, Nauman Shahid, Keith Malcom Smith

Visual short-term memory binding tasks are a promising early marker for Alzheimer's disease (AD). To uncover functional deficits of AD in these tasks it is meaningful to first study unimpaired brain function. Electroencephalogram recordings were obtained f ...

Nature Publishing Group2017

Towards Stationary Time-Vertex Signal Processing

Pierre Vandergheynst, Nathanaël Perraudin, Francesco Grassi, Andreas Loukas

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 networks. Yet, though these systems are often dynamic, state-of-the-art methods ...

Ieee2017

Localization of Sound Sources in a Room with One Microphone

Pierre Vandergheynst, Hervé Lissek, Helena Peic Tukuljac

Estimation of the location of sound sources is usually done using microphone arrays. Such settings provide an environment where we know the difference between the received signals among different microphones in the terms of phase or attenuation, which enab ...

Spie-Int Soc Optical Engineering2017

Geometric deep learning: going beyond Euclidean data

Pierre Vandergheynst, Michael Bronstein

Many signal processing problems involve data whose underlying structure is non-Euclidean, but may be modeled as a manifold or (combinatorial) graph. For instance, in social networks, the characteristics of users can be modeled as signals on the vertices of ...

2017

System, device, and method for contextual knowledge retrieval and display

Pierre Vandergheynst, Kirell Maël Benzi, Benjamin Ricaud

A method to extract patterns of activity from time series of data structured as a network of objects, the method comprising the steps of creating a base graph of similar or related items from a given corpus by comparing intrinsic features of the items, com ...

2017

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

An Algorithm Architecture Co-Design for CMOS Compressive High Dynamic Range Imaging

Pierre Vandergheynst, William Guicquéro, Antoine Dupret

Standard image sensors feature dynamic range about 60 to 70 dB while the light flux of natural scenes may be over 120 dB. Most imagers dedicated to address such dynamic ranges, need specific, and large pixels. However, canonical imagers can be used for hig ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Fast Robust PCA on Graphs

Pierre Vandergheynst, Nathanaël Perraudin, Gilles Puy, Vasilis Kalofolias, Nauman Shahid

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-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

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

A Multiscale Pyramid Transform for Graph Signals

Pierre Vandergheynst, Mohammadjavad Faraji, David Shuman

Multiscale transforms designed to process analog and discrete-time signals and images cannot be directly applied to analyze high-dimensional data residing on the vertices of a weighted graph, as they do not capture the intrinsic topology of the graph data ...

Institute of Electrical and Electronics Engineers2016

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

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Pierre Vandergheynst, Xavier Bresson, Michaël Defferrard

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or wor ...

2016

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

Tensor low-rank and sparse light field photography

Pierre Vandergheynst, Mahdad Hosseini Kamal

High-quality light field photography has been one of the most difficult challenges in computational photography. Conventional methods either sacrifice resolution, use multiple devices, or require multiple images to be captured. Combining coded image acquis ...

Elsevier2016

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

Learning Laplacian Matrix in Smooth Graph Signal Representations

Pascal Frossard, Pierre Vandergheynst, Ntorina Thanou, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not alwa ...

Institute of Electrical and Electronics Engineers2016

Multilinear Low-Rank Tensors on Graphs & Applications

Pierre Vandergheynst, Nauman Shahid, Francesco Grassi

We propose a new framework for the analysis of low- rank tensors which lies at the intersection of spectral graph theory and signal processing. As a first step, we present a new graph based low-rank decomposition which approximates the classical low-rank S ...

2016

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