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 |
ID |
707593 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6x92w0h |