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Machine Learning Methods for High-Dimensional Imbalanced Biomedical Data

Abstract Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical d... (more)
Created Date 2013
Contributor Yang, Tao (Author) / Ye, Jieping (Advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Subject Computer science / Biomedical Data / High-Dimensional / Imbalanced / Machine Learning
Type Masters Thesis
Extent 82 pages
Language English
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis