- #1
Frank Einstein
- 170
- 1
Hello everyone, I have two matrices of size 9*51, meaning that I have 51 measurements of a stochastic process measured at 9 times, being precise, it is wind speed in the direction X, I have the same data for the direction Y. I am aware that both stochastic processes are not independent, so I would like to use PCA over both of them at the same time. My software of choice is Matlab.
I cam perform the PCA analysis simply as:
[coeffU, scoreU, latentU, tsquaredU, explainedU, muU]=pca(U,'Centered',false), however, if I try to execute it over UV, being UV equal to cat(3,U,V), it doesn't work.
Can anyone tell me if there is a way of finding the joint main directions of variation instead of having to compute each one apart?
Thanks for Reading.
I cam perform the PCA analysis simply as:
[coeffU, scoreU, latentU, tsquaredU, explainedU, muU]=pca(U,'Centered',false), however, if I try to execute it over UV, being UV equal to cat(3,U,V), it doesn't work.
Can anyone tell me if there is a way of finding the joint main directions of variation instead of having to compute each one apart?
Thanks for Reading.