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Computer Science > Machine Learning

arXiv:2109.04301 (cs)
[Submitted on 9 Sep 2021]

Title:On the use of Wasserstein metric in topological clustering of distributional data

Authors:Guénaël Cabanes, Younès Bennani, Rosanna Verde, Antonio Irpino
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Abstract:This paper deals with a clustering algorithm for histogram data based on a Self-Organizing Map (SOM) learning. It combines a dimension reduction by SOM and the clustering of the data in a reduced space. Related to the kind of data, a suitable dissimilarity measure between distributions is introduced: the $L_2$ Wasserstein distance. Moreover, the number of clusters is not fixed in advance but it is automatically found according to a local data density estimation in the original space. Applications on synthetic and real data sets corroborate the proposed strategy.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.04301 [cs.LG]
  (or arXiv:2109.04301v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.04301

Submission history

From: Guénaël Cabanes Dr. [view email]
[v1] Thu, 9 Sep 2021 14:27:15 UTC (392 KB)
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