BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
BEGIN:VEVENT
DTSTAMP:20260921T003339Z
UID:Seminar-dept-456@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Lutz Oettershagen:MAILTO:Lutz.Oettershagen@liverpool.ac.uk
DTSTART:20180227T130000
DTEND:20180227T140000
SUMMARY:School Seminar Series
DESCRIPTION:Dr. Sara Kalisnik: A Higher-Dimensional Homologically Persistent Skeleton\n\nA data set is often given as a point cloud, i.e. a non-empty finite metric space. An important problem is to detect the topological shape of data\n\n– for example, to approximate a point cloud by a low-dimensional non-linear subspace such as a graph or a simplicial complex. Classical clustering methods and principal component analysis work very well when data points split into well-separated groups or lie near linear subspaces. Methods from topological data analysis detect more complicated patterns such as holes and voids that persist for a long time in a 1-parameter family of shapes associated to a point cloud. V. Kurlin suggested representing them in a form of a 1-dimensional homologically persistent skeleton, which optimally extends a minimal spanning tree of a point cloud to a graph with cycles. We generalize this skeleton to higher dimensions and prove its optimality among all complexes that preserve topological features of data at any scale.\n\n\n\nThis is joint work with V. Kurlin and D. Lesnik.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=456
LOCATION:Ashton Lecture Theater
END:VEVENT
END:VCALENDAR