Name
Abstract | ||
|---|---|---|
Compressive Sensing (CS) shows that sparse signals can be exactly recovered from a limited number of random or deterministic projections when the measurement mode satisfies some specified conditions. Random matrices, with the drawbacks of large storage, low efficiency and high complexity, are hard to use in practical applications. Recent works explore expander graphs for efficient CS recovery, but there is no explicit construction of expanders. The widely used expanders are chosen at random based on the probabilistic method. In this paper, we propose a parameter based on the second-largest eigenvalue of the adjacency matrix to select optimized expanders from random expanders. The theoretical analysis and the numerical simulations both indicate the selection criteria proposed in this paper can pick up the high-performance expanders from the random expanders effectively. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICInfA.2013.6720446 | Information and Automation |
Keywords | Field | DocType |
second-largest eigenvalue,sparse signals,expander graph,compressive sensing,eigenvalue,matrix algebra,adjacency matrix,probabilistic method,cs recovery,compressed sensing,measurement mode,expander graphs,optimized expanders,random matrices,graph theory,deterministic projections,random expanders,eigenvalues and eigenfunctions,random projections | Adjacency matrix,Graph theory,Mathematical optimization,Expander graph,Matrix algebra,Computer science,Control theory,Algorithm,Probabilistic method,Compressed sensing,Eigenvalues and eigenvectors,Random matrix | Conference |
Volume | Issue | ISSN |
null | null | null |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenghua Wu | 1 | 40 | 5.01 |
| Qiang Wang | 2 | 601 | 84.65 |
| Yi Shen | 3 | 1240 | 70.62 |
| Jie Liu | 4 | 1438 | 94.17 |