Research article
High‐throughput data dimension reduction via seeded canonical correlation analysis
Abstract
Canonical correlation analysis (CCA) is one of popular statistical methodologies in multivariate analysis, especially, in studying relation of two sets of variables. However, if sample sizes are smaller than the maximum of the dimensions of two sets of variables, it is not plausible to construct canonical coefficient matrices due to failure of inverting sample covariance matrices. In this article, we develop a two step procedure of CCA implemented in such situation. For this, seeded dimension reduction is adapted into CCA. Numerical studies confirm the approach, and two real data analyses are presented. Copyright © 2014 John Wiley & Sons, Ltd.
Citing Literature
Number of times cited according to CrossRef: 2
- Keunbaik Lee, Yuri Choi, Hye Yeon Um, Jae Keun Yoo, On fused dimension reduction in multivariate regression, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2019.103828, (103828), (2019).
- Jae Keun Yoo, Partial least squares fusing unsupervised learning, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2017.12.016, 175, (82-86), (2018).
