Non-Convex Tensor Recovery from Tube-Wise Sensing

Tongle Wu, Ying Sun

Keywords: Tensor compressive sensing, non-convex, sample complexity

TL;DR: We introduce the a new local tensor compressive sensing model with computational and statistical guarantees.

Abstract: In this paper, we propose a novel tube-wise local tensor compressed sensing (CS) model, where sensing operators are independently applied to each tube of a third-order tensor. To recover the low-rank ground truth tensor, we minimize a non-convex objective via Burer–Monteiro factorization and solve it using gradient descent with spectral initialization. We prove that this approach achieves exact recovery with a linear convergence rate. Notably, our method attains provably lower sample complexity than existing TCS methods. Our proof leverages the leave-one-out technique to show that gradient descent generates iterates implicitly biased towards solutions with bounded incoherence, which ensures contraction of optimization error in consecutive iterates. Empirical results validate the effectiveness of GD in solving the proposed local TCS model.

Primary Area: Optimization (e.g., convex and non-convex, stochastic, robust)

Submission Number: 8629