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UID:20260624T082121EDT-2968N9gGI7@132.216.98.100
DTSTAMP:20260624T122121Z
DESCRIPTION:Title: Fractal Dimension Estimation with Persistent Homology\n
 \nAbstract: Persistent homology quantifies the shape of a geometric object
  in terms of how its topology changes as it is thickened. Over the past de
 cade there has been a surge of interest in applications of persistent homo
 logy. We propose that the recently defined persistent homology dimensions 
 are a practical tool for fractal dimension estimation of point samples. We
  implement an algorithm to estimate the persistent homology dimension\, an
 d compare its performance to classical methods (e.g. the correlation and b
 ox-counting dimensions) in examples of self-similar fractals\, chaotic att
 ractors\, and an empirical dataset.\n
DTSTART:20191111T210000Z
DTEND:20191111T220000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Jonathan Jaquette (Brandeis University)
URL:/mathstat/channels/event/jonathan-jaquette-brandei
 s-university-302276
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