Semi-Supervised Kernel PCA

Christian Walder, Ricardo Henao, Morten Mørup, Lars Kai Hansen

    Research output: Book/ReportReportResearch

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    We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets.
    Original languageEnglish
    Place of PublicationKgs. Lyngby, Denmark
    PublisherTechnical University of Denmark, DTU Informatics, Building 321
    Publication statusPublished - 2010
    SeriesIMM-Technical Report-2010-10

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