Canonical correlation analysis - remove canonical variates from data X

Hello,

I'm working through canonical correlation analysis and would like to understand whether/how this apply for removing sources from the data input X.

Following this matlab example: (https://se.mathworks.com/help/stats/canoncorr.html)

load carbig;

data = [Displacement Horsepower Weight Acceleration MPG];

nans = sum(isnan(data),2) > 0;

X = data(~nans,1:3);

Y = data(~nans,4:5);

[A,B,r,U,V] = canoncorr(X,Y);

Is it possible to project our the first sources in U from the data matrix X? I am used to doing this with pca - project out sources - but have not understood how to do it with canoncorr, or if it is possible.

Usually from my reduced rank data X, U will have less canonical components than X there are columns in X.

Any help is appreciated.