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SUMMARY:Samplets: Construction and scattered data compression
DTSTART:20230906T150000
DTEND:20230906T160000
DTSTAMP:20260407T064357Z
UID:b4b6d603a23d8823cc4d1f4d8996dd936060c347c86d8cafa25cb87b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Michael Multerer - USI\nWe introduce the concept of samp
 lets by transferring the construction of Tausch-White wavelets to scattere
 d data. The result is a multiresolution analysis tailored to discrete data
  which directly enables data compression\, feature detection and adaptivit
 y.\nThe cost for constructing the samplet basis and for the fast samplet t
 ransform\, respectively\, are linear in the size N of the data set. We emp
 loy samplets with vanishing moments to compress kernel matrices for effici
 ent scattered data approximation. The compressed matrices are sparse and h
 ave only O(N log N) entries.\nThe entailed approximation error is controll
 able by the number of vanishing moments. We finish the presententation wit
 h numerical studies for scattered data approximation with sparsity constra
 ints.\n 
LOCATION:CM 0 11 https://plan.epfl.ch/?room==CM%200%2011
STATUS:CONFIRMED
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