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Clustering Linear Models Using Wasserstein Distance

  • Antonio IrpinoEmail author
  • Rosanna Verde
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

This paper deals with the clustering of complex data. The input elements to be clustered are linear models estimated on samples arising from several sub-populations (typologies of individuals). We review the main approaches to the computation of metrics between linear models. We propose to use a Wasserstein based metric for the first time in this field. We show the properties of the proposed metric and an application to real data using a dynamic clustering algorithm.

Keywords

Quantile Function Functional Data Analysis Allocation Function Single Person Household Pivotal Quantity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Dipartimento di Studi Europei e MediterraneiSecond University of NaplesCasertaItaly

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