Approximation Algorithms for Model-Based Compressive Sensing

TL;DR

This paper introduces an approximation-tolerant framework for model-based compressive sensing, enabling efficient sparse recovery with approximate model-projection algorithms, and applies it to the CEMD model for signals with stable nonzero coefficient positions.

Contribution

It develops a new framework that allows approximate solutions to the model-projection problem, broadening the applicability of model-based compressive sensing.

Findings

Provides algorithms for approximate model-projection problems.

Achieves nearly sample-optimal recovery for the CEMD model.

Extends model-CS to a wider class of models with approximate solutions.

Abstract

Compressive Sensing (CS) stipulates that a sparse signal can be recovered from a small number of linear measurements, and that this recovery can be performed efficiently in polynomial time. The framework of model-based compressive sensing (model-CS) leverages additional structure in the signal and prescribes new recovery schemes that can reduce the number of measurements even further. However, model-CS requires an algorithm that solves the model-projection problem: given a query signal, produce the signal in the model that is also closest to the query signal. Often, this optimization can be computationally very expensive. Moreover, an approximation algorithm is not sufficient for this optimization task. As a result, the model-projection problem poses a fundamental obstacle for extending model-CS to many interesting models. In this paper, we introduce a new framework that we call approximation-tolerant model-based compressive sensing. This framework includes a range of algorithms for sparse recovery that require only approximate solutions for the model-projection problem. In essence, our work removes the aforementioned obstacle to model-based compressive sensing, thereby extending the applicability of model-CS to a much wider class of models. We instantiate this new framework for the Constrained Earth Mover Distance (CEMD) model, which is particularly useful for signal ensembles where the positions of the nonzero coefficients do not change significantly as a function of spatial (or temporal) location. We develop novel approximation algorithms for both the maximization and the minimization versions of the model-projection problem via graph optimization techniques. Leveraging these algorithms into our framework results in a nearly sample-optimal sparse recovery scheme for the CEMD model.