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Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning

Lookup NU author(s): Professor Jonathon Chambers

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by Institute of Electrical and Electronics Engineers, 2016.

For re-use rights please refer to the publisher's terms and conditions.


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 l(2)-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.


Publication metadata

Author(s): Dong J, Wang WW, Dai W, Plumbley MD, Han ZF, Chambers J

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Signal Processing

Year: 2016

Volume: 64

Issue: 2

Pages: 417-431

Print publication date: 15/01/2016

Online publication date: 28/09/2015

Acceptance date: 10/09/2015

Date deposited: 09/02/2017

ISSN (print): 1053-587X

ISSN (electronic): 1941-0476

Publisher: Institute of Electrical and Electronics Engineers

URL: http://dx.doi.org/10.1109/TSP.2015.2483480

DOI: 10.1109/TSP.2015.2483480

Data Source Location: http://dx.doi.org/10.15126/surreydata.00808101


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