Seminar in Random Tensors

Date: November 9, 2020

Time: 11:00AM - 12:00PM

Location: zoom

Speaker: Liza Rebrova, UCLA

Title: About modewise tensor dimension reduction and fitting low-rank tensors

Abstract: It is not a secret that probabilistic view in general and random matrix theory in particular present amazing tools to understand large high-dimensional data. However, in many cases, one has to go beyond “simple” matrix models to correctly represent and treat the data. For example, inherently multimodal data is better represented with a tensor, that is, higher-order generalization of a matrix. Transition to more advanced data structures sometimes can survive re-using old algorithms, however, the development of the special tools that honor the full structure within the data pays off by making the algorithms both much more efficient and better interpretable. In this talk, I will focus on our new provable methods for modewise (structure preserving) tensor dimension reduction. I will also discuss its application to the tensor fitting problem and the connections to interpretable learning from multi-modal data through tensor decompositions.