Semi-supervised classification with spectral subspace projection of data

Weiwei Du, Kiichi Urahama

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)


    A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.

    Original languageEnglish
    Pages (from-to)374-377
    Number of pages4
    JournalIEICE Transactions on Information and Systems
    Issue number1
    Publication statusPublished - Jan 2007

    All Science Journal Classification (ASJC) codes

    • Software
    • Hardware and Architecture
    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering
    • Artificial Intelligence


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