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    Discriminative orthogonal neighborhood-preserving projections for classification

    Zhang, T. and Huang, K. and Li, Xuelong and Yang, J. and Tao, D. (2010) Discriminative orthogonal neighborhood-preserving projections for classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40 (1), pp. 253-263. ISSN 1083-4419.

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    Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.


    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 20 Jun 2013 12:41
    Last Modified: 09 Aug 2023 12:33


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