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dc.contributor.authorZhu, Xiaofeng
dc.contributor.authorXie, Qing
dc.contributor.authorZhu, Yonghua
dc.contributor.authorLiu, Xingyi
dc.contributor.authorZhang, Shichao
dc.date.accessioned2015-05-31T08:53:01Z
dc.date.available2015-05-31T08:53:01Z
dc.date.issued2015-05-28
dc.identifier.citationMulti-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification 2015 Neurocomputing
dc.identifier.issn09252312
dc.identifier.doi10.1016/j.neucom.2014.08.106
dc.identifier.urihttp://hdl.handle.net/10754/556097
dc.description.abstractThis paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0925231215006852
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 28 May 2015. DOI: 10.1016/j.neucom.2014.08.106
dc.subjectImage classification
dc.subjectMulti-view classification
dc.subjectSparse coding
dc.subjectStructure sparsity
dc.subjectReproducing kernel Hilbert space
dc.titleMulti-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalNeurocomputing
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Mathematics and Statistics, Xi'an Jiaotong University, P. R. China
dc.contributor.institutionGuangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, P. R. China
dc.contributor.institutionSchool of Computer, Electronics and Information, Guangxi University, China
dc.contributor.institutionQinzhou Institute of Socialism, Qinzhou, Guangxi, China
kaust.personXie, Qing
refterms.dateFOA2017-05-28T00:00:00Z


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