Effective modeling of high dimensional data is crucial in information processing and machine learning. Classical subspace methods have been very effective in such applications. However, over the past few decades, there has been considerable research towards the development of new modeling paradigms that go beyond subspace methods. This dissertation focuses on the study of sparse models and their interplay with modern machine learning techniques such as manifold, ensemble and graph-based methods, along with their applications in image analysis and recovery.
Download count: 0
- Partial requirement for: Ph.D., Arizona State University, 2013Note typethesis
- Includes bibliographical references (p. 143-157)Note typebibliography
- Field of study: Electrical engineering