Search Results for author: Hermina Petric Maretic

Found 7 papers, 4 papers with code

FGOT: Graph Distances based on Filters and Optimal Transport

1 code implementation9 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.

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

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.

Graph Laplacian mixture model

1 code implementation23 Oct 2018 Hermina Petric Maretic, Pascal Frossard

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets.

Graph Learning

Graph learning under sparsity priors

1 code implementation18 Jul 2017 Hermina Petric Maretic, Dorina Thanou, Pascal Frossard

If this is not possible, the data structure has to be inferred from the mere signal observations.

Graph Learning

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