MATLAB: How to apply The Kaiser rule in PCA

statistics

Hi,

In MATLAB there is a bulid function to apply principle component analysis PCA. However, I have a problem on applying The Kaiser rule which drop all components with eigenvalues under 1. For Example I want to apply this method on the data:

X = [1 2 3 4 5 ; -1 -3 -1 2 4 ; -2 1.5 3 2 -9 ; 1 -1 0.25 2.3 2.2];
[coeff,newdata,latend,tsd,variance] = pca(X)

Please can some one help me on this. Many thanks.

Best Answer

Hey!

Instead of calculating PCA go with SVD. Take under the under root of sigmas 's' and now you have eigenvalues. Check for kaiser rule and select the column with eigenvalue less than 1

X = [1 2 3 4 5 ; -1 -3 -1 2 4 ; -2 1.5 3 2 -9 ; 1 -1 0.25 2.3 2.2];
[u,s,v] = svd(X)

Please refer to this link for further information

https://www.mathworks.com/help/matlab/ref/double.svd.html

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MATLAB: Biplot understanding

Hi, In the first biplot both component 1 and component 2 (the columns of your matrix) are in the same direction and magnitude.

In the second example, biplot() makes the largest magnitude coefficient (element) in each column positive. That is the sign convention. So the -6 in the second column becomes positive and all the other elements in the 2nd column change sign to be negative.

That does not affect the interpretation, which demonstrates that x(end,1) and x(end,2) differ from the relationship between x(1:end-1,1) and x(1:end-1,2)

Wayne

Best Answer

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