Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning

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Title: Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning
Author(s): Dong, J
Wang, W
Dai, W
Plumbley, MD
Han, Z-F
Chambers, J
Item Type: Journal Article
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.
Publication Date: 28-Sep-2015
Date of Acceptance: 10-Sep-2015
ISSN: 1941-0476
Publisher: IEEE
Start Page: 417
End Page: 431
Journal / Book Title: IEEE Transactions on Signal Processing
Volume: 64
Issue: 2
Copyright Statement: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Science & Technology
Engineering, Electrical & Electronic
Analysis dictionary learning
analysis model
sparse representation
Networking & Telecommunications
MD Multidisciplinary
Publication Status: Published
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering

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