Eloi Tanguy

dblp:352/5619 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › topological data analysis
persistence diagram
1.012026
Robust Barycenters of Persistence Diagrams · IEEE Trans. Vis. Comput. Graph. 2026
Visualization and visual analytics
topological data analysis
1.012026
Robust Barycenters of Persistence Diagrams · IEEE Trans. Vis. Comput. Graph. 2026
Data mining
clustering
0.312026
Robust Barycenters of Persistence Diagrams · IEEE Trans. Vis. Comput. Graph. 2026

Methods — techniques the papers use, named apart from their topics

wasserstein barycenter · 2.0optimal transport · 2.0fixed-point method · 2.0
YearPublicationVenuePosition
2026 Robust Barycenters of Persistence Diagrams
abstract
This short paper presents a general approach for computing robust Wasserstein barycenters (Agueh et al. 2011), (Turner et al. 2014),(Vidal et al. 2020) of persistence diagrams. The classical method consists in computing assignment arithmetic means after finding the optimal transport plans between the barycenter and the persistence diagrams. However, this procedure only works for the transportation cost related to the $q$q-Wasserstein distance $W_{q}$Wq when $q=2$q=2. We adapt an alternative fixed-point method (Tanguy et al. 2025) to compute a barycenter diagram for generic transportation costs ($q > 1$q>1), in particular those robust to outliers, $q \in (1,2)$q∈(1,2). We show the utility of our work in two applications: (i) the clustering of persistence diagrams on their metric space and (ii) the dictionary encoding of persistence diagrams (Sisouk et al. 2024). In both scenarios, we demonstrate the added robustness to outliers provided by our generalized framework.
Keanu Sisouk, Eloi Tanguy, Julie Delon, Julien Tierny
IEEE Trans. Vis. Comput. Graph.2