| Publication Type | poster |
| School or College | College of Engineering |
| Department | Kahlert School of Computing |
| 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 |
| 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 | ©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 |