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CATEGORIES:Cambridge Image Analysis Seminars
SUMMARY:Clustering of Big Data: consistency of a nonlocal
Ginzburg-Landau type model - Riccardo Cristoferi\,
Heriot-Watt University
DTSTART;TZID=Europe/London:20190301T160000
DTEND;TZID=Europe/London:20190301T170000
UID:TALK120937AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/120937
DESCRIPTION:The analysis of Big Data is one of the most import
ant challenges of the modern era. A first step in
order to extract some information from a set of da
ta is to partition it according to some notion of
similarity. When only geometric features are used
to define such a notion of similarity and no a pri
ori knowledge of the data is available\, we refer
to it as the clustering problem.\n\nTypically this
labelling task is fulfilled via a minimization pr
ocedure. Of capital importance for evaluating a cl
ustering method is whether it is consistent or not
\; namely it is desirable that the minimization pr
ocedure approaches some limit minimization method
when the number of elements of the data set goes t
o infinity.\n\nIn this talk the consistency of a n
onlocal anisotropic Ginzburg-Landau type functiona
l for clustering is presented. In particular\, it
is proved that the discrete model converges\, in t
he sense of Gamma-convergence\, to a weighted anis
otropic perimeter.\n\nThe talk is based on a work
in collaboration with Matthew Thorpe (Cambridge Un
iversity).
LOCATION:MR 11\, Centre for Mathematical Sciences
CONTACT:Yury Korolev
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