Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/11779
Title: | Sparse Canonical Correlation Analysis (SCCA): A Comparative Study |
Authors: | Pichika, Sathish chandra |
Advisor: | Beyene, Joseph Narayanaswamy Balakrishnan and Aaron Childs |
Department: | Mathematics and Statistics |
Keywords: | CCA;SCCA;High-Dimensional;Multivariate Analysis;Multivariate Analysis |
Publication Date: | Apr-2012 |
Abstract: | <p>Canonical Correlation Analysis (CCA) is one of the multivariate statistical methods that can be used to find relationship between two sets of variables. I highlighted challenges in analyzing high-dimensional data with CCA. Recently, Sparse CCA (SCCA) methods have been proposed to identify sparse linear combinations of two sets of variables with maximal correlation in the context of high-dimensional data. In my thesis, I compared three different SCCA approaches. I evaluated the three approaches as well as the classical CCA on simulated datasets and illustrated the methods with publicly available genomic and proteomic datasets.</p> |
URI: | http://hdl.handle.net/11375/11779 |
Identifier: | opendissertations/6721 7714 2424107 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
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fulltext.pdf | 448.26 kB | Adobe PDF | View/Open |
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