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SUMMARY:Martina Scolamiero (KTH)
DTSTART:20231108T100000Z
DTEND:20231108T110000Z
DTSTAMP:20260423T021036Z
UID:CompAlg/28
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/CompAlg/28/"
 >Machine Learning with Topological Data Analysis features</a>\nby Martina 
 Scolamiero (KTH) as part of Machine Learning Seminar\n\n\nAbstract\nIn Top
 ological Data Analysis\, Persistent Homology has been widely used to extra
 ct features from data. Such features are then used for clustering\, visual
 ization and classification. In this talk I will describe how we define Lip
 schitz continuous persistence features starting from pseudo metrics to com
 pare topological representations of data. Special emphasis will be on the 
 variety of different features that can be constructed in this way and how 
 they can be used in machine learning pipelines. Joint work with the TDA gr
 oup at KTH.\n
LOCATION:https://researchseminars.org/talk/CompAlg/28/
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