Enhanced spectral embedding with semi-supervised feature selection

Weiwei Du, Kiichi Urahama

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

    Abstract

    We present a spectral embedding 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 dataseis.

    Original languageEnglish
    Title of host publication5th International Conference on Natural Computation, ICNC 2009
    Pages129-133
    Number of pages5
    DOIs
    Publication statusPublished - 2009
    Event5th International Conference on Natural Computation, ICNC 2009 - Tianjian, China
    Duration: Aug 14 2009Aug 16 2009

    Publication series

    Name5th International Conference on Natural Computation, ICNC 2009
    Volume1

    Other

    Other5th International Conference on Natural Computation, ICNC 2009
    Country/TerritoryChina
    CityTianjian
    Period8/14/098/16/09

    All Science Journal Classification (ASJC) codes

    • Computational Theory and Mathematics
    • Computer Science Applications
    • Software

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