Sketched approximation of regularized canonical correlation analysis

Jiamin Liu, Wangli Xu, Hongmei Lin and Heng Lian

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 19, 6960-6971

Abstract: Canonical correlation analysis (CCA) is a popular statistical tool in multivariate analysis. A regularized version is often used to stabilize the estimate. Motivated by recent interests in sketching estimates for linear regression problems which try to address the computational problem associated with massive data sets, here we investigate the sketched estimation for CCA, which includes the random subsampling approach as a special case. Some theoretical results are established based on perturbation theory. The method is also illustrated via some Monte Carlo studies and a real data analysis.

Date: 2023
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DOI: 10.1080/03610926.2022.2037644

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