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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jing Dong Wenwu Wang Wei Dai Plumbley, M.D. Zi-Fa Han Chambers, J. |
| Copyright Year | 1991 |
| Abstract | In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the sparse analysis model. This algorithm assumes that the analysis dictionary contains unit l2-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to several existing analysis dictionary learning algorithms, dictionaries learned by the proposed algorithm may contain similar atoms, leading to a degenerate (coherent) dictionary. To address this problem, we also consider restricting the coherence of the learned dictionary and propose Incoherent Analysis SimCO by introducing an atom decorrelation step following the update of the dictionary. We demonstrate the competitive performance of the proposed algorithms using experiments with synthetic data and image denoising as compared with existing algorithms. |
| Sponsorship | IEEE Signal Processing Society |
| Starting Page | 417 |
| Ending Page | 431 |
| Page Count | 15 |
| File Size | 6717471 |
| File Format | |
| ISSN | 1053587X |
| Volume Number | 64 |
| Issue Number | 2 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2016-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Dictionaries Algorithm design and analysis Signal processing algorithms Analytical models Transforms Training Manifolds sparse representation Analysis dictionary learning analysis model SimCO |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering |
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