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dc.contributor.authorLi, Qinfeng*
dc.contributor.authorZhou, Xiaofeng*
dc.contributor.authorGu, Aihua*
dc.contributor.authorLi, Zonghua*
dc.contributor.authorLiang, Ru-Ze*
dc.date.accessioned2017-01-02T08:42:38Z
dc.date.available2017-01-02T08:42:38Z
dc.date.issued2016-11-18en
dc.identifier.citationLi Q, Zhou X, Gu A, Li Z, Liang R-Z (2016) Nuclear norm regularized convolutional Max Pos@Top machine. Neural Computing and Applications. Available: http://dx.doi.org/10.1007/s00521-016-2680-2.en
dc.identifier.issn0941-0643en
dc.identifier.issn1433-3058en
dc.identifier.doi10.1007/s00521-016-2680-2en
dc.identifier.urihttp://hdl.handle.net/10754/622226
dc.description.abstractIn this paper, we propose a novel classification model for the multiple instance data, which aims to maximize the number of positive instances ranked before the top-ranked negative instances. This method belongs to a recently emerged performance, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose an algorithm to learn the convolutional filters and the full connection weights to maximize the Pos@Top measure over the training set. Also, we try to minimize the rank of the filter matrix to explore the low-dimensional space of the instances in conjunction with the classification results. The rank minimization is conducted by the nuclear norm minimization of the filter matrix. In addition, we develop an iterative algorithm to solve the corresponding problem. We test our method on several benchmark datasets. The experimental results show the superiority of our method compared with other state-of-the-art Pos@Top maximization methods.en
dc.description.sponsorshipThe Foundation of modern educational technology research of Jiangsu Province (No. 2015-R-42631) and The University Natural Science Foundation of Jiangsu Province (14KJD520003).en
dc.publisherSpringer Natureen
dc.relation.urlhttp://dx.doi.org/10.1007/s00521-016-2680-2en
dc.subjectMultivariate performance measureen
dc.subjectPositives at Topen
dc.subjectConvolutional networken
dc.subjectMultiple instancesen
dc.subjectNuclear norm regularizationen
dc.titleNuclear norm regularized convolutional Max Pos@Top machineen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division*
dc.contributor.departmentMaterials Science and Engineering Program*
dc.identifier.journalNeural Computing and Applicationsen
dc.contributor.institutionCollege of Computer and Information, Hohai University, Nanjing, China*
dc.contributor.institutionDepartment of Basic, Jinling Institute of Technology, Nanjing, China*
kaust.authorLiang, Ru-Ze*


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