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    Robust learning of discriminative projection for multicategory classification on the Stiefel manifold

    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. Robust learning of discriminative projection for multicategory classification on the stiefel manifold, 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.2008.4587407
    ISBN
    9781424422432
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/9720
    Collection
    • Curtin Research Publications
    Abstract

    Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.

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