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dc.contributor.authorZhang, Miaohua
dc.contributor.authorGao, Yongsheng
dc.contributor.authorSun, Changming
dc.contributor.authorBlumenstein, Michael
dc.date.accessioned2019-06-26T00:22:29Z
dc.date.available2019-06-26T00:22:29Z
dc.date.issued2018
dc.identifier.isbn9781479970612
dc.identifier.issn1522-4880
dc.identifier.doi10.1109/icip.2018.8451746
dc.identifier.urihttp://hdl.handle.net/10072/385800
dc.description.abstractThe orthogonal matching pursuit (OMP) is an important sparse approximation algorithm to recover sparse signals from compressed measurements. However, most MP algorithms are based on the mean square error(MSE) to minimize the recovery error, which is suboptimal when there are outliers. In this paper, we present a new robust OMP algorithm based on kernel non-second order statistics (KNS-OMP), which not only takes advantages of the outlier resistance ability of correntropy but also further extends the second order statistics based correntropy to a non-second order similarity measurement to improve its robustness. The resulted framework is more accurate than the second order ones in reducing the effect of outliers. Experimental results on synthetic and real data show that the proposed method achieves better performances compared with existing methods.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2018 25th IEEE International Conference on Image Processing (ICIP)
dc.relation.ispartofconferencetitle2018 25th IEEE International Conference on Image Processing (ICIP)
dc.relation.ispartofdatefrom2018-10-07
dc.relation.ispartofdateto2018-10-10
dc.relation.ispartoflocationAthens, Greece
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto5
dc.relation.ispartofvolume2018-Oct
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleMatching Pursuit Based on Kernel Non-Second Order Minimization
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorGao, Yongsheng
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorZhang, Lena


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