Is it possible to index which variables to extract after Principle Component Analysis?
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Hi All,
I am a little confused as to how I can determine which variables I want after running my data matrix X through the pca funtion.
[coeff,score,latent,tsquared,explained,mu] = pca(X);
I have a 1000x16 dataset. I know from the 'explained' output that 95% of my feature variability can be explained by 7 variables.
Is there a way of determining which 7 variables (which columns numbers from the original X matrix) these are through indexing from the original X matrix,coeff, scores matrix?
Any help would be greatly appreciated here.
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Accepted Answer
the cyclist
on 13 Jan 2017
Edited: the cyclist
on 14 Jan 2017
It will not be 7 of your original variable that explain 95% of the variation. This is an important concept to understand.
Rather, it will be 7 of the new variables, each of which is a linear combination of your original variables, that will explain the variation. Each column of coeff is the weight of the original variable. The first 7 columns of coeff are what you want.
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