Hybrid Wasserstein distance and fast distribution clustering

DOI10.1214/19-EJS1639zbMath1435.62249arXiv1812.11026OpenAlexW2995577776MaRDI QIDQ2283573

Isabella Verdinelli, Larry Alan Wasserman

Publication date: 3 January 2020

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1812.11026

zbMATH Keywords

clusteringWasserstein distance\(k\)-means

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Minimax confidence intervals for the sliced Wasserstein distance, On the use of Wasserstein distance in the distributional analysis of human decision making under uncertainty, Central limit theorems for semi-discrete Wasserstein distances, Network Consensus in the Wasserstein Metric Space of Probability Measures, On clustering uncertain and structured data with Wasserstein barycenters and a geodesic criterion for the number of clusters

Uses Software

• energy
• k-means++

Cites Work

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