Non-Convex Tensor Recovery from Local Measurements

Motivated by the settings where sensing the entire tensor is infeasible, this paper proposes a novel tensor compressed sensing model, where measurements are only obtained from sensing each lateral slice via mutually independent matrices. Leveraging the low tubal rank structure, we reparameterize the unknown tensor ${\boldsymbol {\mathcal X}}^\star$ using two compact tensor factors and formulate the recovery problem as a nonconvex minimization problem. To solve the problem, we first propose an alternating minimization algorithm, termed \textsf{Alt-PGD-Min}, that iteratively optimizes the two factors using a projected gradient descent and an exact minimization step, respectively. Despite nonconvexity, we prove that \textsf{Alt-PGD-Min} achieves $ε$-accuracy recovery with $\mathcal O\left( κ^2 \log \frac{1}ε\right)$ iteration complexity and $\mathcal O\left( κ^6rn_3\log n_3 \left( κ^2r\left(n_1 + n_2 \right) + n_1 \log \frac{1}ε\right) \right)$ sample complexity, where $κ$ denotes tensor condition number of $\boldsymbol{\mathcal X}^\star$. To further accelerate the convergence, especially when the tensor is ill-conditioned with large $κ$, we prove \textsf{Alt-ScalePGD-Min} that preconditions the gradient update using an approximate Hessian that can be computed efficiently. We show that \textsf{Alt-ScalePGD-Min} achieves $κ$ independent iteration complexity $\mathcal O(\log \frac{1}ε)$ and improves the sample complexity to $\mathcal O\left( κ^4 rn_3 \log n_3 \left( κ^4r(n_1+n_2) + n_1 \log \frac{1}ε\right) \right)$. Experiments validate the effectiveness of the proposed methods.