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    Binary two-dimensional PCA

    Pang, Y. and Tao, D. and Yuan, Y. and Li, Xuelong (2008) Binary two-dimensional PCA. Transactions on Systems, Man, and Cybernetics 38 (4), pp. 1176-1180. ISSN 1083-4419.

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    Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical correspondence first proposes binary 2DPCA (B-2DPCA). Empirical studies demonstrated the advantages of B-2DPCA compared with 2DPCA and binary PCA.


    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 12 Jul 2013 13:19
    Last Modified: 11 Oct 2016 15:27


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