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SUMMARY:Geometric graph-based Methods for High Dimensional Data - Andrea B
 ertozzi (University of California\, Los Angeles)
DTSTART:20211123T163000Z
DTEND:20211123T173000Z
UID:TALK164869@talks.cam.ac.uk
DESCRIPTION:We present new methods for segmentation of large datasets with
  graph based structure. The method combines ideas from classical nonlinear
  PDE-based image segmentation with fast and accessible linear algebra meth
 ods for computing information about the spectrum of the graph Laplacian. T
 he goal of the algorithms is to solve semi-supervised and unsupervised gra
 ph cut optimization problems. I will present results for image processing 
 applications such as image labeling and hyperspectral video segmentation\,
  and results from machine learning and community detection in social netwo
 rks\, including modularity optimization posed as a graph total variation m
 inimization problem.
LOCATION:Seminar Room 1\, Newton Institute
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