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UID:7433@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20171204T140000
DTEND;TZID=Europe/Paris:20171204T150000
DTSTAMP:20241120T203952Z
URL:https://www.i2m.univ-amu.fr/evenements/robust-clustering-tools-based-o
 n-optimal-transportation/
SUMMARY: (...): Robust clustering tools based on optimal transportation
DESCRIPTION:: A robust clustering method for probabilities in Wasserstein s
 pace is introduced. This new `trimmed $k$-barycenters' approach relies on 
 recent results on barycenters in Wasserstein space that allow intensive co
 mputation\, as required by clustering algorithms. The possibility of trimm
 ing the most discrepant distributions results in a gain in stability and r
 obustness\, highly convenient in this setting. As a remarkable application
  we consider a parallelized estimation setup in which each of m units proc
 esses a portion of the data\, producing an estimate of $k$-features\, enco
 ded as $k$ probabilities. We prove that the trimmed $k$-barycenter of the 
 $m\\times k$ estimates produces a consistent aggregation. We illustrate th
 e methodology with simulated and real data examples.http://www.eio.uva.es/
 infor/personas/tasio.html
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DTSTART:20171029T020000
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