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SUMMARY:Object Data Driven Discovery - Ian Dryden (University of Nottingha
 m)
DTSTART:20180320T143000Z
DTEND:20180320T153000Z
UID:TALK102676@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Object data analysis is an important tool in the many discipli
 nes where the data have much richer structure than the usual numbers or ve
 ctors. An initial question to ask is: what are the most basic data units? 
 i.e. what are the atoms of the data? We describe an introduction to this t
 opic\,  where the statistical analysis of object data has a wide variety o
 f applications. An important aspect of the analysis is to reduce the dimen
 sion to a small number key features while respecting the geometry of the m
 anifold in which objects lie. Three case studies are given which exemplify
  the types of issues that are encountered: i) Describing changes in variab
 ility in damaged DNA\, ii) Testing for geometrical differences in carotid 
 arteries\, where patients are at high or low risk of aneurysm\, iii) clust
 ering enzymes observed over time. In all three applications the structure 
 of the data manifolds is important\, in particular the manifold of covaria
 nce matrices\, unlabelled size-and-shape space and shape space.
LOCATION:Seminar Room 1\, Newton Institute
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