Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel approach to convolutional sparse representation with the aim of resolving the dictionary learning problem. The proposed method, referred to as the adaptive alternating direction method of multipliers (AADMM), employs constraints comprising non-convex, nonsmooth terms, such as the ℓ0-norm imposed on the coefficients and the unit-norm sphere imposed on the length of... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIP.2019.2896541 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Dictionaries,Signal processing algorithms,Convergence,Convolution,Machine learning,Matching pursuit algorithms,Optimization | Convergence (routing),Dictionary learning,Pattern recognition,Sparse approximation,Artificial intelligence,Image signal,Mathematics | Journal |
Volume | Issue | ISSN |
28 | 7 | 1057-7149 |
Citations | PageRank | References |
1 | 0.36 | 20 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guan-Ju Peng | 1 | 11 | 3.27 |