I am working with a dataset of around 4000 variables. I decided to carry out a principal component analysis (PCA) for the data, but I am not quite sure about the suitable number of variables I should include in the test.
Would feeding a big number (such as 4000) variables interfere with the PCA accuracy? As far as I can understand from the definition and the way the PCA is conducted, the number of variables should not matter, but I am not 100% sure and I couldn't find any source talking about the effect of number of variables on PCA.
My question in simple terms would be: should I just include all the variables I have into the PCA test or should I do other tests to reduce the number of variables a little before doing the PCA?