Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other.
Download count: 0
- Partial requirement for: Ph.D., Arizona State University, 2013Note typethesis
- Includes bibliographical references (p. 84-95)Note typebibliography
- Field of study: Computer science