Dimensionality reduction for semi-supervised face recognition

Weiwei Du, Kohei Inoue, Kiichi Urahama

    Research output: Contribution to journalConference articlepeer-review

    6 Citations (Scopus)


    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
    Pages (from-to)1-10
    Number of pages10
    JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
    Issue numberPART II
    Publication statusPublished - 2005
    EventSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China
    Duration: Aug 27 2005Aug 29 2005

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)


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