Name
Title | ||
|---|---|---|
Conjugate Gradient Iterative Hard Thresholding: Observed Noise Stability for Compressed Sensing |
Abstract | ||
|---|---|---|
Conjugate gradient iterative hard thresholding (CGIHT) for compressed sensing combines the low per iteration computational cost of simple line search iterative hard thresholding algorithms with the improved convergence rates of more sophisticated sparse approximation algorithms. This paper shows that the average case performance of CGIHT is robust to additive noise well beyond its theoretical worst case guarantees and, in this setting, is typically the fastest iterative hard thresholding algorithm for sparse approximation. Moreover, CGIHT is observed to benefit more than other iterative hard thresholding algorithms when jointly considering multiple sparse vectors whose sparsity patterns coincide. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TSP.2014.2379665 | Signal Processing, IEEE Transactions |
Keywords | Field | DocType |
awgn,compressed sensing,conjugate gradient methods,numerical stability,cgiht,additive noise stability,conjugate gradient iterative hard thresholding,improved convergence rates,iteration computational cost,multiple sparse vector,simple line search iterative hard thresholding algorithm,sparse approximation algorithm,additive noise,data collection,iterative decoding,row-sparse approximation,numerical analysis | Convergence (routing),Conjugate gradient method,Mathematical optimization,Noise stability,Pattern recognition,Sparse approximation,Line search,Artificial intelligence,Thresholding,Numerical analysis,Compressed sensing,Mathematics | Journal |
Volume | Issue | ISSN |
63 | 2 | 1053-587X |
Citations | PageRank | References |
11 | 0.60 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey D. Blanchard | 1 | 168 | 10.45 |
| Jared Tanner | 2 | 525 | 42.48 |
| Ke Wei | 3 | 131 | 7.79 |