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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- Partial requirement for: Ph.D., Arizona State University, 2011Note typethesis
- Includes bibliographical references (p. 165-186)Note typebibliography
- Field of study: Computer science