712 citations · 1.1k across the 13 of their papers we have counts for
13 papers
Joint Sparse Recovery Using Signal Space Matching Pursuit
Junhan Kim, Jian Wang, Luong Trung Nguyen +1
In this paper, we put forth a new joint sparse recovery algorithm called signal space matching pursuit (SSMP). The key idea of the proposed SSMP algorithm is to sequentially invest…
On the Fundamental Recovery Limit of Orthogonal Least Squares
Junhan Kim, Jian Wang, Byonghyo Shim
Orthogonal least squares (OLS) is a classic algorithm for sparse recovery, function approximation, and subset selection. In this paper, we analyze the performance guarantee of the…
A Sharp Condition for Exact Support Recovery of Sparse Signals With Orthogonal Matching Pursuit
JInming Wen, Zhengchun Zhou, Jian Wang +2
Support recovery of sparse signals from noisy measurements with orthogonal matching pursuit (OMP) has been extensively studied in the literature. In this paper, we show that for an…
Optimal Power Control for Transmitting Correlated Sources with Energy Harvesting Constraints
Yunquan Dong, Zhi Chen, Jian Wang +1
We investigate the weighted-sum distortion minimization problem in transmitting two correlated Gaussian sources over Gaussian channels using two energy harvesting nodes. To this en…
Nearly Optimal Bounds for Orthogonal Least Squares
Jinming Wen, Jian Wang, Qinyu Zhang
In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On the one hand, we show that if the sampling matrix satisfies the restricted…
A Sharp Condition for Exact Support Recovery of with Orthogonal Matching Pursuit
Jinming Wen, Zhengchun Zhou, Jian Wang +2
Support recovery of sparse signals from noisy measurements with orthogonal matching pursuit (OMP) has been extensively studied. In this paper, we show that for any -sparse signa…
Support Recovery with Orthogonal Matching Pursuit in the Presence of Noise: A New Analysis
Jian Wang
Support recovery of sparse signals from compressed linear measurements is a fundamental problem in compressed sensing (CS). In this paper, we study the orthogonal matching pursuit…
Recovery of Sparse Signals Using Multiple Orthogonal Least Squares
Jian Wang, Ping Li
We study the problem of recovering sparse signals from compressed linear measurements. This problem, often referred to as sparse recovery or sparse reconstruction, has generated a…
Multipath Matching Pursuit
Suhyuk, Kwon, Jian Wang +1
In this paper, we propose an algorithm referred to as multipath matching pursuit that investigates multiple promising candidates to recover sparse signals from compressed measureme…
Recovery of Sparse Signals via Generalized Orthogonal Matching Pursuit: A New Analysis
Jian Wang, Suhyuk Kwon, Ping Li +1
As an extension of orthogonal matching pursuit (OMP) improving the recovery performance of sparse signals, generalized OMP (gOMP) has recently been studied in the literature. In th…
Exact Recovery of Sparse Signals via Orthogonal Matching Pursuit: How Many Iterations Do We Need?
Jian Wang, Byonghyo Shim
Orthogonal matching pursuit (OMP) is a greedy algorithm widely used for the recovery of sparse signals from compressed measurements. In this paper, we analyze the number of iterati…
Generalized Orthogonal Matching Pursuit
Jian Wang, Seokbeop Kwon, Byonghyo Shim
As a greedy algorithm to recover sparse signals from compressed measurements, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper…
A Simple Proof of the Mutual Incoherence Condition for Orthogonal Matching Pursuit
Jian Wang, Byonghyo Shim
This paper provides a simple proof of the mutual incoherence condition under which K-sparse signal can be accurately reconstructed from a small number of linear…