BIROn - Birkbeck Institutional Research Online

    Semisupervised dimensionality reduction and classification through virtual label regression

    Nie, F. and Xu, D. and Li, Xuelong and Xiang, S. (2011) Semisupervised dimensionality reduction and classification through virtual label regression. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41 (3), pp. 675-685. ISSN 1083-4419.

    Full text not available from this repository.


    Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.


    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 13:40
    Last Modified: 09 Aug 2023 12:33


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item