Publications de Mireille El Gheche

Distributed Graph Learning With Smooth Data Priors

Pascal Frossard, Isabela Cunha Maia Nobre, Mireille El Gheche

Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...

IEEE2022

fGOT: Graph Distances Based on Filters and Optimal Transport

Pascal Frossard, Giovanni Chierchia, Hermina Petric Maretic, Mireille El Gheche

Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and inexpensive comparison metrics. In this work we introduce the filter graph dis ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Wasserstein-Based Graph Alignment

Pascal Frossard, Hermina Petric Maretic, Mireille El Gheche

A novel method for comparing non-aligned graphs of various sizes is proposed, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices. Specifically, a new formulation for the one-to-many graph ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Multilayer Graph Clustering With Optimized Node Embedding

Pascal Frossard, Mireille El Gheche

We are interested in multilayer graph clustering, which aims at dividing the graph nodes into categories or communities. To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fid ...

IEEE2021

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

Giovanni Chierchia, Mireille El Gheche

Proximal splitting methods are standard tools for nonsmooth optimization. While primal-dual methods have become very popular in the last decade for their flexibility, primal methods may still be preferred for two reasons: acceleration schemes are more effe ...

IEEE2021

Figlearn: Filter And Graph Learning Using Optimal Transport

Pascal Frossard, Matthias Minder, Mireille El Gheche, Zahra Farsijani

In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise that has been filtered on the graph topology by a graph filter. Hence, the k ...

IEEE2021

Forward-Backward Splitting for Optimal Transport Based Problems

Pascal Frossard, Effrosyni Simou, Hermina Petric Maretic, Mireille El Gheche, Guillermo Ortiz Jimenez

Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given cost, over the space of doubly stochastic matrices. When the entropy regula ...

IEEE2020

Joint Graph-based Depth Refinement and Normal Estimation

Pascal Frossard, Mattia Rossi, Mireille El Gheche

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a prevalence of human made structures, which in most of the cases, are either ...

2020

OrthoNet: Multilayer Network Data Clustering

Pascal Frossard, Giovanni Chierchia, Mireille El Gheche

Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

GOT: An Optimal Transport framework for Graph comparison

Pascal Frossard, Giovanni Chierchia, Hermina Petric Maretic, Mireille El Gheche

We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph signals defined with respect to the graph topology. This allows us to derive an ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Une approche basée incertitude pour transporter un signal d’un graphe à un autre

Pascal Frossard, Hermina Petric Maretic, Mireille El Gheche, Bastien Cyril Pasdeloup

2019

Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

Pascal Frossard, Giovanni Chierchia, Mireille El Gheche

In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its application to larg ...

IEEE2019

Graph Heat Mixture Model Learning

Pascal Frossard, Hermina Petric Maretic, Mireille El Gheche

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all avai ...

IEEE2018

A Nonsmooth Graph-Based Approach to Light Field Super-Resolution

Pascal Frossard, Mattia Rossi, Mireille El Gheche

In this article we propose a new super-resolution algorithm tailored for light field cameras, which suffer by design from a limited spatial resolution. To do so, we cast the light field super-resolution problem into an optimization problem, where the parti ...

2018

Proximity Operators of Discrete Information Divergences

Giovanni Chierchia, Mireille El Gheche

While phi-divergences have been extensively studied in convex analysis, their use in optimization problems often remains challenging. In this regard, one of the main shortcomings of existing methods is that the minimization of phi-divergences is usually pe ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Robust and adaptive approaches for Relative Geologic Time Estimation

Mireille El Gheche

For a geoscientist, the Relative Geologic Time (RGT) is an important tool to perform chronostratigraphic analysis. However, automatically estimate an RGT image from a seismic image can be a challenging task where we have to respect seismic features, the de ...

ELSEVIER SCIENCE BV2018