Computing hulls in positive definite space

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Publication Type poster
School or College Scientific Computing and Imaging Institute
Department Computing, School of
Creator Fletcher, Preston Thomas; Moeller, John Henry; Phillips, Jeffrey; Venkatasubramanian, Suresh
Title Computing hulls in positive definite space
Date 2010-10-06
Description P(n): a Riemannian manifold Definition: symmetric positive-definite (n) (n) matrices Applications: Diffusion Tensor MRI (DT-MRI) Flow through voxel modeled in P(3) Elasticity Tensors Modeled by elements of P(6) Machine Learning Used in kernels Convex Hulls Data on P(n): Want to analyze this data Centerpoints, clustering, shape Convex hull (CH) is a useful data analysis tool Describes shape of the data Can use max CH peeling depth to find a centerpoint A framework for analyzing shape in spaces where CH is difficult to work with (ball hull) An approximation to the ball hull (""-ball hull) A way to measure width as a side benefit (extent) Horofunctions provide a good way to analyze manifolds like this
Type Text; Image
Publisher University of Utah
Language eng
Bibliographic Citation Fletcher, Preston Thomas; Moeller, John Henry; Phillips, Jeffrey; Venkatasubramanian, Suresh (2010). Computing hulls in positive definite space. University of Utah.
Rights Management (c)P. Thomas Fletcher, John Moeller, Jeff M. Phillips, Suresh Venkatasubramanian
Format Medium application/pdf
Format Extent 136,902 bytes
Identifier ir-main/14814
ARK ark:/87278/s6x92w0h
Setname ir_uspace
Date Created 2012-07-30
Date Modified 2021-05-06
ID 707593
Reference URL https://collections.lib.utah.edu/ark:/87278/s6x92w0h
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