- #1
confused_engineer
- 39
- 2
- TL;DR Summary
- I cannot calculate the random variables associated to the eigenfuctions of the principal component analysis.
Hello.
I have designed a Gaussian kernel as:
[X,Y] = meshgrid(0:0.002:1,0:0.002:1);
Z=exp((-1)*abs(X-Y));
Now, I calculate PCA:
[coeffG, scoreG, latentG, tsquaredG, explainedG, muG]=pca(Z, 'Centered',false);
I can rebuid the original data propperly as defined in the dcumentation https://la.mathworks.com/help/stats/pca.html#bth9ibe-coeff:
Zrec=scoreG*coeffG';
Now, I wish to calculate the uncorrelated random variables which acompany the eigenvectors, columns of scoreG. For that, I do the following:
randvarG=Z*scoreG(:,1); However, the resulting histogram of randvarG does not look gaussian at all.
Any hindsight on what I am doing wrong is very appreciated.
Thanks for reading
I have designed a Gaussian kernel as:
[X,Y] = meshgrid(0:0.002:1,0:0.002:1);
Z=exp((-1)*abs(X-Y));
Now, I calculate PCA:
[coeffG, scoreG, latentG, tsquaredG, explainedG, muG]=pca(Z, 'Centered',false);
I can rebuid the original data propperly as defined in the dcumentation https://la.mathworks.com/help/stats/pca.html#bth9ibe-coeff:
Zrec=scoreG*coeffG';
Now, I wish to calculate the uncorrelated random variables which acompany the eigenvectors, columns of scoreG. For that, I do the following:
randvarG=Z*scoreG(:,1); However, the resulting histogram of randvarG does not look gaussian at all.
Any hindsight on what I am doing wrong is very appreciated.
Thanks for reading