Description

Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these

Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented.

Reuse Permissions
  • 4.22 MB application/pdf

    Download count: 0

    Details

    Contributors
    Date Created
    • 2013
    Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: M.S., Arizona State University, 2013
      Note type
      thesis
    • Includes bibliographical references (p. 59-65)
      Note type
      bibliography
    • Field of study: Computer science

    Citation and reuse

    Statement of Responsibility

    by Tao Yang

    Machine-readable links