<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> / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/11779 |
| Date | 04 1900 |
| Creators | Pichika, Sathish chandra |
| Contributors | Beyene, Joseph, Narayanaswamy Balakrishnan and Aaron Childs, Mathematics and Statistics |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
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