A robust matching pursuit algorithm using information theoretic learning

Zhang, M; Gao, Y; Sun, C; Blumenstein, M

A robust matching pursuit algorithm using information theoretic learning

Zhang, M Gao, Y Sun, C Blumenstein, M

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Pattern Recognition, 2020, 107, pp. 107415-107415
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Zhang, M
dc.contributor.author	Gao, Y
dc.contributor.author	Sun, C
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2020-10-19T21:47:34Z
dc.date.available	2020-10-19T21:47:34Z
dc.date.issued	2020-11-01
dc.identifier.citation	Pattern Recognition, 2020, 107, pp. 107415-107415
dc.identifier.issn	0031-3203
dc.identifier.uri	http://hdl.handle.net/10453/143384
dc.description.abstract	© 2020 Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are both suboptimal when there are non-Gaussian noises or outliers in the observation data. To overcome these problems, a new OMP algorithm is developed based on information theoretic learning (ITL), which is built on the following new techniques: (1) an ITL-based correlation (ITL-Correlation) is developed as a new similarity measure which can better exploit higher-order statistics of the data, and is robust against many different types of noise and outliers in a sparse representation framework; (2) a non-second order statistic measurement and minimization method is developed to improve the robustness of OMP by overcoming the limitation of Gaussianity inherent in a cost function based on second-order moments. The experimental results on both simulated and real-world data consistently demonstrate the superiority of the proposed OMP algorithm in data recovery, image reconstruction, and classification.
dc.language	en
dc.publisher	Elsevier BV
dc.relation	http://purl.org/au-research/grants/arc/DP140101075
dc.relation.ispartof	Pattern Recognition
dc.relation.isbasedon	10.1016/j.patcog.2020.107415
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A robust matching pursuit algorithm using information theoretic learning
dc.type	Journal Article
utslib.citation.volume	107
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-19T21:47:24Z
pubs.publication-status	Accepted
pubs.volume	107

Abstract:

© 2020 Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are both suboptimal when there are non-Gaussian noises or outliers in the observation data. To overcome these problems, a new OMP algorithm is developed based on information theoretic learning (ITL), which is built on the following new techniques: (1) an ITL-based correlation (ITL-Correlation) is developed as a new similarity measure which can better exploit higher-order statistics of the data, and is robust against many different types of noise and outliers in a sparse representation framework; (2) a non-second order statistic measurement and minimization method is developed to improve the robustness of OMP by overcoming the limitation of Gaussianity inherent in a cost function based on second-order moments. The experimental results on both simulated and real-world data consistently demonstrate the superiority of the proposed OMP algorithm in data recovery, image reconstruction, and classification.

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http://hdl.handle.net/10453/143384