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    Semi-supervised sparse coding

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    3_IJCNN.pdf
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    Type
    Conference Paper
    Authors
    Wang, Jim Jing-Yan
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2014-09-10
    Preprint Posting Date
    2013-11-26
    Online Publication Date
    2014-09-10
    Print Publication Date
    2014-07
    Permanent link to this record
    http://hdl.handle.net/10754/556650
    
    Metadata
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    Abstract
    Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.
    Citation
    Wang, J. J.-Y., & Gao, X. (2014). Semi-supervised sparse coding. 2014 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2014.6889449
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2014 International Joint Conference on Neural Networks (IJCNN)
    Conference/Event name
    2014 International Joint Conference on Neural Networks, IJCNN 2014
    DOI
    10.1109/IJCNN.2014.6889449
    arXiv
    1311.6834
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6889449
    http://arxiv.org/abs/1311.6834
    ae974a485f413a2113503eed53cd6c53
    10.1109/IJCNN.2014.6889449
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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