Name
Abstract | ||
|---|---|---|
This paper proposes a novel matching pursuit generalized approximate message passing (MPGAMP) algorithm which explores the support of sparse representation coefficients step by step, and estimates the mean and variance of non-zero elements at each step based on a generalized-approximate-message-passing-like scheme. In contrast to the classic message passing based algorithms and matching pursuit based algorithms, our proposed algorithm saves a lot of intermediate process memory, and does not calculate the inverse matrix. Numerical experiments show that MPGAMP algorithm can recover a sparse signal from compressed sensing measurements very well, and maintain good performance even for non-zero mean projection matrix and strong correlated projection matrix. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1587/transfun.E98.A.2723 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
compressed sensing, generalized approximate message passing, matching pursuit, robust | Matching pursuit,Algorithm,Theoretical computer science,Message passing,Mathematics,Compressed sensing | Journal |
Volume | Issue | ISSN |
E98A | 12 | 0916-8508 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong-Jie Luo | 1 | 2 | 1.05 |
| Qun Wan | 2 | 15 | 5.14 |
| Guan Gui | 3 | 641 | 102.53 |
| Fumiyuki Adachi | 4 | 1588 | 195.77 |