Name
Abstract | ||
|---|---|---|
The problem of sparse signal reconstruction in the presence of possibly impulsive noise is studied. The state-of-the-art greedy algorithms, Iterative Hard Thresholding (IHT), Orthogonal Matching Pursuit (OMP), and Compressive Sampling Matching Pursuit (CoSaMP) are robustified in order to cope with impulsive noise environments and outliers. We employ robust weighting of the residuals and replace the least-squares estimates by M-estimates of regression. Also a robust M-estimation based ridge regression is considered and shown to possess high potential when utilized in CS algorithms. |
Year | Venue | Keywords |
|---|---|---|
2012 | Signal Processing Conference | greedy algorithms,iterative methods,least squares approximations,regression analysis,signal reconstruction,signal sampling,CoSaMP,IHT,OMP,compressed sensing,compressive sampling matching pursuit,impulsive noise environments,impulsive noise outliers,iterative hard thresholding,least-squares estimates,orthogonal matching pursuit,regression M-estimates,robust M-estimation based ridge regression,robust greedy algorithms,sparse signal reconstruction,compressive sensing,greedy algorithms,matching pursuit |
Field | DocType | ISSN |
Matching pursuit,Weighting,Pattern recognition,Iterative method,Outlier,Greedy algorithm,Artificial intelligence,Thresholding,Signal reconstruction,Compressed sensing,Mathematics | Conference | 2219-5491 |
ISBN | Citations | PageRank |
978-1-4673-1068-0 | 11 | 0.66 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seyed Alireza Razavi | 1 | 42 | 7.77 |
| E. Ollila | 2 | 40 | 2.14 |
| Visa Koivunen | 3 | 11 | 0.66 |