Name
Title | ||
|---|---|---|
Joint and Direct Optimization for Dictionary Learning in Convolutional Sparse Representation. |
Abstract | ||
|---|---|---|
Convolutional sparse coding (CSC) is a useful tool in many image and audio applications. Maximizing the performance of CSC requires that the dictionary used to store the features of signals can be learned from real data. The so-called convolutional dictionary learning (CDL) problem is formulated within a nonconvex, nonsmooth optimization framework. Most existing CDL solvers alternately update the ... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TNNLS.2019.2906074 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Dictionaries,Convolution,Convergence,Convolutional codes,Optimization,Approximation algorithms,Machine learning | Dictionary learning,Computer science,Sparse approximation,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
31 | 2 | 2162-237X |
Citations | PageRank | References |
1 | 0.35 | 23 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guan-Ju Peng | 1 | 11 | 3.27 |