Robust Recovery of Signals From a Structured Union of Subspaces

TL;DR

This paper introduces a robust, convex optimization-based framework for recovering signals lying in a union of subspaces, generalizing compressed sensing techniques to structured, multi-subspace models with stability guarantees.

Contribution

It develops a block-sparse recovery approach using a mixed $/$ program and establishes RIP-based conditions for guaranteed, stable recovery of structured signals from samples.

Findings

Proposes a block-sparse recovery algorithm for union of subspaces.

Derives RIP-based conditions ensuring exact recovery.

Extends results to joint sparsity in MMV problems.

Abstract

Traditional sampling theories consider the problem of reconstructing an unknown signal $x$ from a series of samples. A prevalent assumption which often guarantees recovery from the given measurements is that $x$ lies in a known subspace. Recently, there has been growing interest in nonlinear but structured signal models, in which $x$ lies in a union of subspaces. In this paper we develop a general framework for robust and efficient recovery of such signals from a given set of samples. More specifically, we treat the case in which $x$ lies in a sum of $k$ subspaces, chosen from a larger set of $m$ possibilities. The samples are modelled as inner products with an arbitrary set of sampling functions. To derive an efficient and robust recovery algorithm, we show that our problem can be formulated as that of recovering a block-sparse vector whose non-zero elements appear in fixed blocks. We then propose a mixed $\ell_2/\ell_1$ program for block sparse recovery. Our main result is an equivalence condition under which the proposed convex algorithm is guaranteed to recover the original signal. This result relies on the notion of block restricted isometry property (RIP), which is a generalization of the standard RIP used extensively in the context of compressed sensing. Based on RIP we also prove stability of our approach in the presence of noise and modelling errors. A special case of our framework is that of recovering multiple measurement vectors (MMV) that share a joint sparsity pattern. Adapting our results to this context leads to new MMV recovery methods as well as equivalence conditions under which the entire set can be determined efficiently.