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dc.contributor.authorYu, Jun
dc.contributor.author俞俊
dc.contributor.authorTao, Dacheng
dc.contributor.authorWang, Meng
dc.date.accessioned2013-03-19T00:44:58Z
dc.date.available2013-03-19T00:44:58Z
dc.date.issued2012-07
dc.identifier.citationIEEE TRANSACTIONS ON IMAGE PROCESSING,2012,21(7):3262-3272zh_CN
dc.identifier.issn1057-7149
dc.identifier.urihttp://dx.doi.org/10.1109/TIP.2012.2190083
dc.identifier.uriWOS:000305577600013
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/15161
dc.description.abstractRecent years have witnessed a surge of interest in graph-based transductive image classification. Existing simple graph-based transductive learning methods only model the pairwise relationship of images, however, and they are sensitive to the radius parameter used in similarity calculation. Hypergraph learning has been investigated to solve both difficulties. It models the high-order relationship of samples by using a hyperedge to link multiple samples. Nevertheless, the existing hypergraph learning methods face two problems, i.e., how to generate hyperedges and how to handle a large set of hyperedges. This paper proposes an adaptive hypergraph learning method for transductive image classification. In our method, we generate hyperedges by linking images and their nearest neighbors. By varying the size of the neighborhood, we are able to generate a set of hyperedges for each image and its visual neighbors. Our method simultaneously learns the labels of unlabeled images and the weights of hyperedges. In this way, we can automatically modulate the effects of different hyperedges. Thorough empirical studies show the effectiveness of our approach when compared with representative baselines.zh_CN
dc.description.sponsorshipNational Natural Science Foundation of China [61100104]; Australian Research Council [120103730]zh_CN
dc.language.isoenzh_CN
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCzh_CN
dc.subjectClassificationzh_CN
dc.subjecthypergraphzh_CN
dc.subjecttransductive learningzh_CN
dc.titleAdaptive Hypergraph Learning and its Application in Image Classificationzh_CN
dc.typeArticlezh_CN


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