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    Nuclear norm regularized convolutional Max Pos@Top machine

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    Type
    Article
    Authors
    Li, Qinfeng
    Zhou, Xiaofeng
    Gu, Aihua
    Li, Zonghua
    Liang, Ru-Ze cc
    KAUST Department
    Material Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2016-11-18
    Online Publication Date
    2016-11-18
    Print Publication Date
    2018-07
    Permanent link to this record
    http://hdl.handle.net/10754/622226
    
    Metadata
    Show full item record
    Abstract
    In 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.
    Citation
    Li 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.
    Sponsors
    The Foundation of modern educational technology research of Jiangsu Province (No. 2015-R-42631) and The University Natural Science Foundation of Jiangsu Province (14KJD520003).
    Publisher
    Springer Nature
    Journal
    Neural Computing and Applications
    DOI
    10.1007/s00521-016-2680-2
    Additional Links
    http://dx.doi.org/10.1007/s00521-016-2680-2
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00521-016-2680-2
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Material Science and Engineering Program

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