Dimensionality reduction for semi-supervised face recognition

Weiwei Du, Kohei Inoue, Kiichi Urahama

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


    A dimensionality reduction technique is presented for semi-supervised face recognition where image data are mapped into a low dimensional space with a spectral method. A mapping of learning data is generalized to a new datum which is classified in the low dimensional space with the nearest neighbor rule. The same generalization is also devised for regularized regression methods which work in the original space without dimensionality reduction. It is shown with experiments that the spectral mapping method outperforms the regularized regression. A modification scheme for data similarity matrices on the basis of label information and a simple selection rule for data to be labeled are also devised.

    Original languageEnglish
    Title of host publicationFuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings
    PublisherSpringer Verlag
    Number of pages10
    ISBN (Print)9783540283317
    Publication statusPublished - 2006
    Event2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsa, China
    Duration: Aug 27 2005Aug 29 2005

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3614 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Other2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)


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