Covering point-sets with parallel hyperplanes and sparse signal recovery

TL;DR

This paper introduces a new deterministic method for constructing integer sensing matrices for compressed sensing, based on geometric properties of point sets and hyperplanes, enabling efficient sparse signal recovery.

Contribution

It presents a novel construction of integer sensing matrices derived from point-set configurations that cannot be covered by few parallel hyperplanes, linking geometry with compressed sensing.

Findings

Constructed integer matrices with bounded sup-norm and linear independence properties.

Demonstrated matrices' effectiveness for sparse signal recovery in compressed sensing.

Connected matrix construction to the Tarski plank problem.

Abstract

We give a new deterministic construction of integer sensing matrices that can be used for the recovery of integer-valued signals in compressed sensing. This is a family of $n \times d$ integer matrices, $d \geq n$, with bounded sup-norm and the property that no $\ell$ column vectors are linearly dependent, $\ell \leq n$. Further, if $\ell \leq o(\log n)$ then $d/n \to \infty$ as $n \to \infty$. Our construction comes from particular sets of difference vectors of point-sets in $\mathbb R^n$ that cannot be covered by few parallel hyperplanes. We construct examples of such sets on the $0, \pm 1$ grid and use them for the matrix construction. We also show a connection of our constructions to a simple version of the Tarski plank problem.