Semi-supervised spectral mapping for enhancing separation between classes

Weiwei Du, Kiichi Urahama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We present a spectral mapping technique for semisupervised pattern classification. Importance scores of features are firstly evaluated with a semi-supervised feature selection algorithm by Zhao et al. Training data are then embedded into a low-dimensional space with a spectral mapping derived from the selected and weighted feature vectors with which test data are classified by the nearest neighbor rule. The performance of the proposed pattern classification algorithm is examined with synthetic and real datasets.

    Original languageEnglish
    Title of host publicationProceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
    Pages187-190
    Number of pages4
    Publication statusPublished - Dec 1 2009
    Event11th IAPR Conference on Machine Vision Applications, MVA 2009 - Yokohama, Japan
    Duration: May 20 2009May 22 2009

    Publication series

    NameProceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009

    Other

    Other11th IAPR Conference on Machine Vision Applications, MVA 2009
    Country/TerritoryJapan
    CityYokohama
    Period5/20/095/22/09

    All Science Journal Classification (ASJC) codes

    • Computer Vision and Pattern Recognition

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