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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180525T110000
DTEND;TZID=America/New_York:20180525T120000
DTSTAMP:20180521T190200
CREATED:20180510T154321Z
LAST-MODIFIED:20180515T185748Z
UID:7554-1527246000-1527249600@idss.mit.edu
SUMMARY:Stochastics and Statistics Seminar Series: 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 manifold $M_o$ 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://idss.mit.edu/calendar/stochastics-and-statistics-seminar-8/
CATEGORIES:Stochastics and Statistics Seminar Series
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