Robust clustering tools based on optimal transportation

DOI10.1007/s11222-018-9800-zzbMath1430.62130arXiv1607.01179OpenAlexW3101366889MaRDI QIDQ2329755

Carlos Matrán, Eustasio del Barrio, Juan Antonio Cuesta-Albertos, Agustín Mayo-Iscar

Publication date: 18 October 2019

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1607.01179

zbMATH Keywords

Wasserstein distanceMonge-Kantorovich problemrobust aggregationtransport mapstrimmed distributionsparallelized inferencetrimmed barycenter\( k\)-barycenterBraggingcluster prototypessubraggingtrimmed \(k\)-means algorithm

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (9)

Stochastic approximation versus sample average approximation for Wasserstein barycenters ⋮ Limit laws for empirical optimal solutions in random linear programs ⋮ Wide consensus aggregation in the Wasserstein space. Application to location-scatter families ⋮ Spatial prediction and spatial dependence monitoring on georeferenced data streams ⋮ Statistical inference for Bures-Wasserstein barycenters ⋮ Metrics and barycenters for point pattern data ⋮ Hybrid Wasserstein distance and fast distribution clustering ⋮ On clustering uncertain and structured data with Wasserstein barycenters and a geodesic criterion for the number of clusters ⋮ K-bMOM: A robust Lloyd-type clustering algorithm based on bootstrap median-of-means

Uses Software

• flowClust
• TCLUST

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