Adaptive ADMM for Dictionary Learning in Convolutional Sparse Representation
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, non-smooth ter...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 28(2019), 7 vom: 04. Juli, Seite 3408-3422 |
|---|---|
| 1. Verfasser: | |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2019
|
| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | 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, non-smooth terms, such as the l0 -norm imposed on the coefficients and the unit-norm sphere imposed on the length of each dictionary element. The proposed scheme incorporates a novel parameter adaption scheme that enables ADMM to achieve convergence more quickly, as evidenced by numerical and theoretical analysis. In experiments involving image signal applications, the dictionaries learned using AADMM outperformed those learned using comparable dictionary learning methods |
|---|---|
| Beschreibung: | Date Revised 20.11.2019 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2019.2896541 |