Decomposing Low-Rank Symmetric Tensors

Joe Kileel (The University of Texas at Austin)

In this talk, I will discuss low-rank decompositions of symmetric tensors (a.k.a. higher-order symmetric matrices).  I will start by sketching how results in algebraic geometry imply uniqueness guarantees for tensor decompositions, and also lead to fast and numerically stable algorithms for calculating the decompositions.  Then I will quantify the associated non-convex optimization landscapes.  Finally, I will present applications to Gaussian mixture models in data science, and rigid motion segmentation in computer vision.  Based on joint works with João M. Pereira, Timo Klock and Tammy Kolda.

Start date
Tuesday, Feb. 8, 2022, 1:25 p.m.
End date
Tuesday, Feb. 8, 2022, 2:25 p.m.

Walter Library 402