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    Regularized maximum correntropy machine

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    RegMaxCEM_NEUCOM.pdf
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    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Wang, Jim Jing-Yan
    Wang, Yunji
    Jing, Bing-Yi
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2015-02-12
    Online Publication Date
    2015-02-12
    Print Publication Date
    2015-07
    Permanent link to this record
    http://hdl.handle.net/10754/577311
    
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    Abstract
    In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.
    Citation
    Regularized maximum correntropy machine 2015, 160:85 Neurocomputing
    Publisher
    Elsevier BV
    Journal
    Neurocomputing
    DOI
    10.1016/j.neucom.2014.09.080
    arXiv
    1501.04282
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S0925231215001150
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.neucom.2014.09.080
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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