Name
Abstract | ||
|---|---|---|
Dictionary learning algorithms have received widespread acceptance when it comes to data analysis and signal representations problems. These algorithms consist of two stages: the sparse coding stage and dictionary update stage. This latter stage can be achieved sequentially or in parallel. In this work, the maximum likelihood approach is used to derive a new approach to dictionary learning. The proposed method differs from recent dictionary learning algorithms for sparse representation by updating all the dictionary atoms in parallel using only one eigen-decomposition. The effectiveness of the proposed method is tested on two different image processing applications: filling-in missing pixels and noise removal. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SSP.2014.6884595 | Statistical Signal Processing |
Keywords | Field | DocType |
eigenvalues and eigenfunctions,image coding,image representation,learning (artificial intelligence),matrix decomposition,maximum likelihood estimation,data analysis,dictionary atoms,dictionary update stage,eigen-decomposition,filling-in missing pixels,image processing,maximum likelihood orthogonal dictionary learning algorithm,noise removal,signal representations problems,sparse coding stage,sparse representation,Dictionary learning,maximum likelihood,parallel update | K-SVD,Pattern recognition,Computer science,Maximum likelihood,Speech recognition,Artificial intelligence | Conference |
Citations | PageRank | References |
4 | 0.42 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Hanif | 1 | 207 | 25.54 |
| Abd-Krim Seghouane | 2 | 78 | 12.27 |