Search Results for author: Mireille El Gheche

Found 12 papers, 2 papers with code

Distributed Graph Learning with Smooth Data Priors

no code implementations11 Dec 2021 Isabela Cunha Maia Nobre, Mireille El Gheche, Pascal Frossard

We propose here a novel distributed graph learning algorithm, which permits to infer a graph from signal observations on the nodes under the assumption that the data is smooth on the target graph.

Distributed Optimization Graph Learning +1

FGOT: Graph Distances based on Filters and Optimal Transport

2 code implementations9 Sep 2021 Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

We tackle the problem of graph alignment by computing graph permutations that minimise our new filter distances, which implicitly solves the graph comparison problem.

Multilayer Graph Clustering with Optimized Node Embedding

no code implementations30 Mar 2021 Mireille El Gheche, Pascal Frossard

To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fidelity term to the layers of a given multilayer graph, and a regularization on the (single-layer) graph induced by the embedding.

Clustering Graph Clustering

FiGLearn: Filter and Graph Learning using Optimal Transport

no code implementations29 Oct 2020 Matthias Minder, Zahra Farsijani, Dhruti Shah, Mireille El Gheche, Pascal Frossard

We cast a new optimisation problem that minimises the Wasserstein distance between the distribution of the signal observations and the filtered signal distribution model.

Graph Learning

Multilayer Clustered Graph Learning

no code implementations29 Oct 2020 Mireille El Gheche, Pascal Frossard

In this paper, we aim at analyzing multilayer graphs by properly combining the information provided by individual layers, while preserving the specific structure that allows us to eventually identify communities or clusters that are crucial in the analysis of graph data.

Clustering Graph Learning

Wasserstein-based Graph Alignment

no code implementations12 Mar 2020 Hermina Petric Maretic, Mireille El Gheche, Matthias Minder, Giovanni Chierchia, Pascal Frossard

We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices.

Graph Classification

Joint Graph-based Depth Refinement and Normal Estimation

no code implementations CVPR 2020 Mattia Rossi, Mireille El Gheche, Andreas Kuhn, Pascal Frossard

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings.

Depth Estimation

Forward-Backward Splitting for Optimal Transport based Problems

no code implementations20 Sep 2019 Guillermo Ortiz-Jimenez, Mireille El Gheche, Effrosyni Simou, Hermina Petric Maretic, Pascal Frossard

Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset.

Domain Adaptation

Graph heat mixture model learning

no code implementations24 Jan 2019 Hermina Petric Maretic, Mireille El Gheche, Pascal Frossard

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.

OrthoNet: Multilayer Network Data Clustering

no code implementations2 Nov 2018 Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

We propose in this paper to extend the node clustering problem, that commonly considers only the network information, to a problem where both the network information and the node features are considered together for learning a clustering-friendly representation of the feature space.

Clustering Graph Clustering +1

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