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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Manifold Data Analysis with Applications to High-R
esolution 3D Imaging - Matthew Reimherr (Pennsylva
nia State University)
DTSTART;TZID=Europe/London:20180320T100000
DTEND;TZID=Europe/London:20180320T110000
UID:TALK102658AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/102658
DESCRIPTION:Many scientific areas are faced with the challenge
of extracting information from large\, complex\,
and highly structured data sets. A great deal of m
odern statistical work focuses on developing tools
for handling such data. In this work we presents
a new subfield of functional data analysis\, FDA\,
which we call Manifold Data Analysis\, or MDA. MD
A is concerned with the statistical analysis of sa
mples where one or more variables measured on each
unit is a manifold\, thus resulting in as many ma
nifolds as we have units. We propose a framework t
hat converts manifolds into functional objects\, a
n efficient 2-step functional principal component
method\, and a manifold-on-scalar regression model
. This work is motivated by an anthropological ap
plication involving 3D facial imaging data\, which
is discussed extensively throughout. The propose
d framework is used to understand how individual c
haracteristics\, such as age and genetic ancestry\
, influence the shape of the human face.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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