In this talk, we will present an asymptotic analysis of large asymmetric spiked tensor models, relying on tools of 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 of a low-rank spiked model with non-independent components, by studying two types of deflation procedures and proposing an improved algorithm.
Sylvain Carrozza, Luca Lionni, Fabien Vignes-Tourneret