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CATEGORIES:Applied and Computational Analysis
SUMMARY:Geometric graph-based methods for high dimensional
data - Andrea Bertozzi (Mathematics\, UCLA)
DTSTART;TZID=Europe/London:20151102T160000
DTEND;TZID=Europe/London:20151102T170000
UID:TALK61923AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/61923
DESCRIPTION:We present new methods for segmentation of large d
atasets with graph based structure. The method com
bines ideas from classical nonlinear PDE-based ima
ge segmentation with fast and accessible linear al
gebra methods for computing information about the
spectrum of the graph Laplacian. The goal of the a
lgorithms is to solve semi-supervised and unsuperv
ised graph cut optimization problems. I will prese
nt results for image processing applications such
as image labeling and hyperspectral video segmenta
tion\, and results from machine learning and commu
nity detection in social networks\, including modu
larity optimization posed as a graph total variati
on minimization problem.
LOCATION:MR 13\, CMS
CONTACT:Carola-Bibiane Schoenlieb
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