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dc.contributor.authorZhang, M
dc.contributor.authorGao, Y
dc.contributor.authorSun, C
dc.contributor.authorBlumenstein, M
dc.date.accessioned2021-02-19T00:27:58Z
dc.date.available2021-02-19T00:27:58Z
dc.date.issued2020
dc.identifier.issn0031-3203
dc.identifier.doi10.1016/j.patcog.2020.107415
dc.identifier.urihttp://hdl.handle.net/10072/402374
dc.description.abstractCurrent 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.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom107415
dc.relation.ispartofjournalPattern Recognition
dc.relation.ispartofvolume107
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleA robust matching pursuit algorithm using information theoretic learning
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationZhang, M; Gao, Y; Sun, C; Blumenstein, M, A robust matching pursuit algorithm using information theoretic learning, Pattern Recognition, 2020, 107, pp. 107415
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2021-02-19T00:24:59Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorGao, Yongsheng


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