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SUMMARY:Invariants for multidimensional persistence
DTSTART:20150501T141500
DTEND:20150501T153000
DTSTAMP:20260406T170045Z
UID:701cbe134e3d99c917c80c1393594c4da62e3dac75d2193fc1dab37a
CATEGORIES:Conferences - Seminars
DESCRIPTION:Martina Scolamiero (KTH)\nMultidimensional Persistence is a me
 thod in topological data analysis which allows to study several properties
  of a dataset contemporarily. It is important to identify discrete invaria
 nts for multidimensional persistence in order to compare properties of dif
 ferent datasets. Furthermore such invariants should be stable\, i.e.\, dat
 a sets which are considered to be close should give close values of the in
 variant. We will introduce a framework that allows to compute a new class 
 of stable discrete invariants for multidimensional persistence. In doing t
 his\, we will generalize the notion of interleaving topology on multi- dim
 ensional persistence modules and consequently the notion of closeness for 
 datasets. A filter function is usually chosen to highlight properties we w
 ant to examine from a dataset. Similarly\, our new topology allows some fe
 atures of datasets to be considered as noise. (Joint work with Chachólski
 \, Lundman\, and Öberg.)
LOCATION:CM 9
STATUS:CONFIRMED
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