Tensor estimation with structured priors
Abstract
We consider rank-one symmetric tensor estimation when the tensor is corrupted by Gaussian noise and the spike forming the tensor is a structured signal coming from a generalized linear model. The latter is a mathematically tractable model of a non-trivial hidden lower-dimensional latent structure in a signal. We work in a large dimensional regime with fixed ratio of signal-to-latent space dimensions. Remarkably, in this asymptotic regime, the mutual information between the spike and the observations can be expressed as a finite-dimensional variational problem, and it is possible to deduce the minimum-mean-square-error from its solution. We discuss, on examples, properties of the phase transitions as a function of the signal-to-noise ratio. Typically, the critical signal-to-noise ratio decreases with increasing signal-to-latent space dimensions. We discuss the limit of vanishing ratio of signal-to-latent space dimensions and determine the limiting tensor estimation problem. We also point out similarities and differences with the case of matrices.
Cite
@article{arxiv.2006.14989,
title = {Tensor estimation with structured priors},
author = {Clément Luneau and Nicolas Macris},
journal= {arXiv preprint arXiv:2006.14989},
year = {2020}
}