Efficient and Robust Compressed Sensing using High-Quality Expander Graphs

TL;DR

This paper improves compressed sensing algorithms using high-quality expander graphs, reducing recovery iterations to O(k) and enabling efficient, robust signal reconstruction with fewer measurements and faster algorithms.

Contribution

It introduces a method leveraging expander graphs with high expansion coefficients to significantly reduce recovery iterations and improve efficiency in compressed sensing.

Findings

Recovery can be achieved in at most 2k iterations.

Number of iterations can be made arbitrarily close to k.

The method is robust and extends to approximate sparse signals.

Abstract

Expander graphs have been recently proposed to construct efficient compressed sensing algorithms. In particular, it has been shown that any $n$-dimensional vector that is $k$-sparse (with $k\ll n$) can be fully recovered using $O(k\log\frac{n}{k})$ measurements and only $O(k\log n)$ simple recovery iterations. In this paper we improve upon this result by considering expander graphs with expansion coefficient beyond 3/4 and show that, with the same number of measurements, only $O(k)$ recovery iterations are required, which is a significant improvement when $n$ is large. In fact, full recovery can be accomplished by at most $2k$ very simple iterations. The number of iterations can be made arbitrarily close to $k$, and the recovery algorithm can be implemented very efficiently using a simple binary search tree. We also show that by tolerating a small penalty on the number of measurements, and not on the number of recovery iterations, one can use the efficient construction of a family of expander graphs to come up with explicit measurement matrices for this method. We compare our result with other recently developed expander-graph-based methods and argue that it compares favorably both in terms of the number of required measurements and in terms of the recovery time complexity. Finally we will show how our analysis extends to give a robust algorithm that finds the position and sign of the $k$ significant elements of an almost $k$-sparse signal and then, using very simple optimization techniques, finds in sublinear time a $k$-sparse signal which approximates the original signal with very high precision.