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Structured Sparse Learning and Its Applications to Biomedical and Biological Data


Abstract 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 study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. I... (more)
Created Date 2013
Contributor Yuan, Lei (Author) / Ye, Jieping (Advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Subject Computer science / Developmental biology / Bioinformatics / Structured Sparse Learning
Type Doctoral Dissertation
Extent 109 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Computer Science 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis