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X-WR-CALNAME:MIT Center for Statistics
X-ORIGINAL-URL:https://stat.mit.edu
X-WR-CALDESC:Events for MIT Center for Statistics
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180525T110000
DTEND;TZID=America/New_York:20180525T120000
DTSTAMP:20180524T035940
CREATED:20180402T183905Z
LAST-MODIFIED:20180515T135342Z
UID:2491-1527246000-1527249600@stat.mit.edu
SUMMARY:Fitting a putative manifold to noisy data
DESCRIPTION: Abstract: We give a solution to the following question from manifold learning.\nSuppose data belonging to a high dimensional Euclidean space is drawn independently\, identically distributed from a measure supported on a low dimensional twice differentiable embedded compact manifold M\, and is corrupted by a small amount of i.i.d gaussian noise. How can we produce a manifoldĀ M whose Hausdorff distance to M is small and whose reach (normal injectivity radius) is not much smaller than the reach of M?\nThis is joint work with Charles Fefferman\, Sergei Ivanov\, Yaroslav Kurylev\, and Matti Lassas. \n
URL:https://stat.mit.edu/calendar/stochastics-statistics-seminar-narayanan/
LOCATION:50 Ames Street\, Cambridge\, MA\, 02139
GEO:42.3620185;-71.0878444
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CATEGORIES:Stochastics and Statistics Seminar
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