Determining the number of correlated signals between two data sets using PCA-CCA when sample support is extremely small
Yang Song, Peter J. Schreier, Nicholas J. Roseveare · 2015
This paper is concerned with determining the number of correlated signals between two data sets when the number of samples from these data sets is extremely small. In such a scenario, a principal component analysis (PCA) preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present a reduced-rank version of the hypothesis test based on the Bartlett-Lawley statistic, which allows jointly determining the required PCA dimension reduction and the number of correlated signals.