Deterministic tensor completion with hypergraph expanders

TL;DR

This paper introduces a deterministic approach to low-rank tensor completion using hypergraph expanders, establishing sample complexity bounds and proposing a practical algorithm validated through experiments.

Contribution

It provides the first deterministic analysis of tensor completion, introduces a new expander mixing lemma for hypergraphs, and develops an effective algorithm for max-quasinorm minimization.

Findings

Sample complexity is linear in tensor dimension under certain conditions.

New properties of tensor max-quasinorm are established.

Practical algorithm demonstrates effective tensor recovery in experiments.

Abstract

We provide a novel analysis of low-rank tensor completion based on hypergraph expanders. As a proxy for rank, we minimize the max-quasinorm of the tensor, which generalizes the max-norm for matrices. Our analysis is deterministic and shows that the number of samples required to approximately recover an order-$t$ tensor with at most $n$ entries per dimension is linear in $n$, under the assumption that the rank and order of the tensor are $O(1)$. As steps in our proof, we find a new expander mixing lemma for a $t$-partite, $t$-uniform regular hypergraph model, and prove several new properties about tensor max-quasinorm. To the best of our knowledge, this is the first deterministic analysis of tensor completion. We develop a practical algorithm that solves a relaxed version of the max-quasinorm minimization problem, and we demonstrate its efficacy with numerical experiments.