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Sparse learning is a powerful tool to generate models of high-dimensional data with high interpretability, and it has many important applications in areas such as bioinformatics, medical image processing, and

Sparse learning is a powerful tool to generate models of high-dimensional data with high interpretability, and it has many important applications in areas such as bioinformatics, medical image processing, and computer vision. Recently, the a priori structural information has been shown to be powerful for improving the performance of sparse learning models. A graph is a fundamental way to represent structural information of features. This dissertation focuses on graph-based sparse learning.

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    Date Created
    • 2014
    Resource Type
  • Text
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    • Partial requirement for: Ph.D., Arizona State University, 2014
      Note type
      thesis
    • Includes bibliographical references (p. 121-127)
      Note type
      bibliography
    • Field of study: Computer science

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    by Sen Yang

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