Sampling and reconstructing signals from a union of linear subspaces

TL;DR

This paper demonstrates that simple iterative projection algorithms can effectively recover signals from a union of subspaces in infinite-dimensional Hilbert spaces, generalizing many recent results across compressed sensing and related fields.

Contribution

It introduces a unified framework for sampling and reconstructing signals from infinite unions of subspaces using bi-Lipschitz conditions, applicable in infinite-dimensional settings.

Findings

Iterative projection algorithms successfully recover signals under bi-Lipschitz conditions.

The approach unifies results across finite and infinite unions of subspaces.

Applicable to infinite-dimensional Hilbert spaces in analog compressed sensing.

Abstract

In this note we study the problem of sampling and reconstructing signals which are assumed to lie on or close to one of several subspaces of a Hilbert space. Importantly, we here consider a very general setting in which we allow infinitely many subspaces in infinite dimensional Hilbert spaces. This general approach allows us to unify many results derived recently in areas such as compressed sensing, affine rank minimisation and analog compressed sensing. Our main contribution is to show that a conceptually simple iterative projection algorithms is able to recover signals from a union of subspaces whenever the sampling operator satisfies a bi-Lipschitz embedding condition. Importantly, this result holds for all Hilbert spaces and unions of subspaces, as long as the sampling procedure satisfies the condition for the set of subspaces considered. In addition to recent results for finite unions of finite dimensional subspaces and infinite unions of subspaces in finite dimensional spaces, we also show that this bi-Lipschitz property can hold in an analog compressed sensing setting in which we have an infinite union of infinite dimensional subspaces living in infinite dimensional space.