Date of Award
Spring 2012
Degree Type
Thesis
Degree Name
Master of Science (MSc)
Department
Mathematics and Statistics
Supervisor
Joseph Beyene
Language
English
Committee Member
Narayanaswamy Balakrishnan and Aaron Childs
Abstract
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.
Recommended Citation
Pichika, Sathish chandra, "Sparse Canonical Correlation Analysis (SCCA): A Comparative Study" (2012). Open Access Dissertations and Theses. Paper 6721.
http://digitalcommons.mcmaster.ca/opendissertations/6721
McMaster University Library