Imperial College London
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DrWeiDai

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6333wei.dai1 Website

 
 
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Location

 

811Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Dong:2015:10.1109/TSP.2015.2483480,
author = {Dong, J and Wang, W and Dai, W and Plumbley, MD and Han, Z-F and Chambers, J},
doi = {10.1109/TSP.2015.2483480},
journal = {IEEE Transactions on Signal Processing},
pages = {417--431},
title = {Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning},
url = {http://dx.doi.org/10.1109/TSP.2015.2483480},
volume = {64},
year = {2015}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Dong,J
AU  - Wang,W
AU  - Dai,W
AU  - Plumbley,MD
AU  - Han,Z-F
AU  - Chambers,J
DO  - 10.1109/TSP.2015.2483480
EP  - 431
PY  - 2015///
SN  - 1941-0476
SP  - 417
TI  - Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning
T2  - IEEE Transactions on Signal Processing
UR  - http://dx.doi.org/10.1109/TSP.2015.2483480
UR  - http://hdl.handle.net/10044/1/40316
VL  - 64
ER  -
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