ASU Electronic Theses and Dissertations
- 1 Public
- Computer science
- Developmental biology
- 1 Structured Sparse Learning
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. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I …
- Yuan, Lei, Ye, Jieping, Wang, Yalin, et al.
- Created Date