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.
Download count: 0
- Partial requirement for: Ph.D., Arizona State University, 2014Note typethesis
- Includes bibliographical references (p. 121-127)Note typebibliography
- Field of study: Computer science