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    Joint learning and dictionary construction for pattern recognition

    Access Status
    Fulltext not available
    Authors
    Pham, DucSon
    Venkatesh, Svetha
    Date
    2008
    Type
    Conference Paper
    
    Metadata
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    Citation
    Pham, D. and Venkatesh, S. 2008. Joint learning and dictionary construction for pattern recognition, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 23-28 2008. Anchorage, Alaska: IEEE.
    Source Title
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Source Conference
    CVPR 2008
    DOI
    10.1109/CVPR.2008.4587408
    ISBN
    9781424422432
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/7656
    Collection
    • Curtin Research Publications
    Abstract

    We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

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