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Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study

Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning.

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    Date Created
    • 2011
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  • Text
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    • Partial requirement for: Ph.D., Arizona State University, 2011
      Note type
      thesis
    • Includes bibliographical references (p. 165-186)
      Note type
      bibliography
    • Field of study: Computer science

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    by Liang Sun

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