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.
|Number of pages||7|
|Journal||Journal of Chemometrics|
|State||Published - 1 Mar 2015|
- Canonical correlation analysis
- Large p small n
- Multivariate analysis
- Seeded dimension reduction