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SUMMARY:Randomised dimensionality reduction for persistent homology - Mart
 in Lotz - University of Manchester
DTSTART:20171101T140000Z
DTEND:20171101T150000Z
UID:TALK80351@talks.cam.ac.uk
CONTACT:Rachel Furner
DESCRIPTION:We discuss ways in which ideas arising from compressive sensin
 g and related fields can lead to complexity reductions in topological data
  analysis. In particular\, it is possible to reduce the computation of per
 sistent homology of Euclidean point clouds to spaces whose dimension is pr
 oportional to an intrinsic notion of dimension\, the Gaussian width\, asso
 ciated to structural properties of the data. We give an overview of the re
 levant theory and discuss applications and limitations of this approach.
LOCATION:MR5 Centre for Mathematical Sciences
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