Tel-Aviv University
School of Mathematical Sciences
Department of Applied Mathematics

Applied Mathematics Seminar


Tuesday, December 28th, 2010

Schreiber 309, 15:10



Yaniv Gur

University of Utah



High-order tensor decompositions in neuroimaging

Abstract:

High order tensors are considered as multidimensional arrays or N-mode arrays with more than two dimensions (modes). There are various decompositions associated with high-order tensors. These are widely used to solve problems in different fields such as psychometrics, chemometrics, signal processing, computer vision, data mining and much more. The use of these decompositions in new applications is constantly increasing. In this talk I will focus on the CP decomposition which provides a low-rank tensor approximation as a sum of rank-one tensors. I will show how high-order tensors and CP decompositions can be used to resolve white-matter structure of the brain.

This is a joint work with Chris Johnson, Sarang Joshi and Fangxiang Jiao.