Add to ORKGAbstract—This paper addresses the problem of sparse recovery with graph constraints in the sense that we can take additive measurements over nodes only if they induce a connected sub-graph. We provide explicit measurement constructions for several special graphs. A general measurement construction algorithm is also proposed and evaluated. For any given graph G with n nodes, we derive order optimal upper bounds of the minimum number of measurements needed to recover any k-sparse vector over G (MGk,n). Our study suggests that M G k,n may serve as a graph connectivity metric. I
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Many learning and inference problems involve high-dimensional data such as images, video or genomic ...
Abstract—Five known greedy algorithms designed for the single measurement vector setting in compress...
Abstract—This paper addresses the problem of sparse recovery with graph constraints in the sense tha...
Sparse recovery can recover sparse signals from a set of underdetermined linear measurements. Motiva...
Sparse recovery explores the sparsity structure inside data and aims to find a low-dimensional repre...
Abstract—In this paper, motivated by network inference and tomography applications, we study the pro...
Abstract—We investigate the problem of reconstructing a high-dimensional nonnegative sparse vector f...
Abstract—Expander graphs have been recently proposed to construct efficient compressed sensing algor...
The paper deals with the estimation of the maximal sparsity degree for which a given measurement mat...
Final versionInternational audienceWe study a weaker formulation of the nullspace property which gua...
In this paper, we analyze the information theoretic lower bound on the necessary number of samples n...
We consider a specific graph learning task: reconstructing a symmetric matrix that represents an und...
Abstract: "Previous asymptotically correct algorithms for recovering causal structure from sample pr...
We address the problem of robustly recovering the support of high-dimensional sparse signals1 from l...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Many learning and inference problems involve high-dimensional data such as images, video or genomic ...
Abstract—Five known greedy algorithms designed for the single measurement vector setting in compress...
Abstract—This paper addresses the problem of sparse recovery with graph constraints in the sense tha...
Sparse recovery can recover sparse signals from a set of underdetermined linear measurements. Motiva...
Sparse recovery explores the sparsity structure inside data and aims to find a low-dimensional repre...
Abstract—In this paper, motivated by network inference and tomography applications, we study the pro...
Abstract—We investigate the problem of reconstructing a high-dimensional nonnegative sparse vector f...
Abstract—Expander graphs have been recently proposed to construct efficient compressed sensing algor...
The paper deals with the estimation of the maximal sparsity degree for which a given measurement mat...
Final versionInternational audienceWe study a weaker formulation of the nullspace property which gua...
In this paper, we analyze the information theoretic lower bound on the necessary number of samples n...
We consider a specific graph learning task: reconstructing a symmetric matrix that represents an und...
Abstract: "Previous asymptotically correct algorithms for recovering causal structure from sample pr...
We address the problem of robustly recovering the support of high-dimensional sparse signals1 from l...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Many learning and inference problems involve high-dimensional data such as images, video or genomic ...
Abstract—Five known greedy algorithms designed for the single measurement vector setting in compress...