Analysis of Linear and Nonlinear dimensionality Reduction Methods for Gender Classification of Face Images

S. Buchala, N. Davey, T.M. Gale, R. Frank

    Research output: Contribution to journalArticlepeer-review

    17 Citations (Scopus)
    197 Downloads (Pure)

    Abstract

    Data in many real world applications are high dimensional and learning algorithms like neural networks may have problems in handling high dimensional data. However, the Intrinsic Dimension is often much less than the original dimension of the data. Here, we use fractal based methods to estimate the Intrinsic Dimension and show that a nonlinear projection method called Curvilinear Component Analysis can effectively reduce the original dimension to the Intrinsic Dimension. We apply this approach for dimensionality reduction of the face images data and use neural network classifiers for Gender Classification.
    Original languageEnglish
    Pages (from-to)931-942
    JournalInternational Journal of Systems Science
    Volume36
    Issue number14
    DOIs
    Publication statusPublished - Nov 2005

    Fingerprint

    Dive into the research topics of 'Analysis of Linear and Nonlinear dimensionality Reduction Methods for Gender Classification of Face Images'. Together they form a unique fingerprint.

    Cite this