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Analysis SimCO: A new algorithm for analysis dictionary learning

Dong, J, Wang, W and Dai, W (2014) Analysis SimCO: A new algorithm for analysis dictionary learning

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Abstract

We consider the dictionary learning problem for the analysis model based sparse representation. A novel algorithm is proposed by adapting the synthesis model based simultaneous codeword optimisation (SimCO) algorithm to the analysis model. This algorithm assumes that the analysis dictionary contains unit Ł-norm atoms and trains the dictionary by the optimisation on manifolds. This framework allows one to update multiple dictionary atoms in each iteration, leading to a computationally efficient optimisation process. We demonstrate the competitive performance of the proposed algorithm using experiments on both synthetic and real data, as compared with three baseline algorithms, Analysis K-SVD, analysis operator learning (AOL) and learning overcomplete sparsifying transforms (LOST), respectively. © 2014 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
AuthorsEmailORCID
Dong, JUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Dai, WUNSPECIFIEDUNSPECIFIED
Date : 2014
Identification Number : https://doi.org/10.1109/ICASSP.2014.6854996
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 13:12
Last Modified : 28 Mar 2017 13:12
URI: http://epubs.surrey.ac.uk/id/eprint/806093

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