Asymptotic Analysis of Asymmetric Spiked Tensor Models with Random Matrix Theory

In this talk, we will present an asymptotic analysis of large asymmetric spiked tensor models, relying on tools from random matrix theory. In the first part of the talk, we will provide the analysis of a rank-one model along with some algorithmic implications in terms of possible signal recovery with a polynomial time algorithm. The second part of the talk will present a generalization to a low-rank spiked model with correlated components, by studying a Hotteling-type deflation procedure. [slides]

Presented at the Workshop on Tensor Theory and Methods.