Calculating principal component scores from principal component coefficients of the new data
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Amin Kassab-Bachi
le 7 Mai 2021
Modifié(e) : Amin Kassab-Bachi
le 12 Mai 2021
Hi all,
I perfomed a PCA on dataset using the function
[coeff,score,latent,~,explained,mu]=pca(TrainingSet.X);
Then I generated new shapes (in the cartesian space) using a reduced number of principal components. Now I need to the principal component scores for these new shapes, but I can't figure out how!
Based on the fact that the original centered training data can be retrieved using
centeredData= score*coeff'
I used the following statements, which did not generate relevant results.
for i= 1:newShapesNum
newShapeScore(i,:)=newShape(i,:)*pinv(coeff(:,1:shapeModesNum)'); % i is the counter of new (generated) observations.
newSvalid=newShapeScore(i,:)*coeff(:,1:shapeModesNum)';
end
UPDATE
I also tried running a pca analysis on the new instances, and requested [score] and [coeff]. The mean shape looked good but using the centeredData formula above did not regenerate the original shape! I don't understand why though..
I'd appreciate your help in finding the principal component scores for the new shapes.
Many thanks
Amin
2 commentaires
Aditya Patil
le 11 Mai 2021
Can you elaborate on the issue? Are you trying to convert new data as per the pca transformation? Or is the issue that pca transformation of new data is leading to poor results?
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Aditya Patil
le 12 Mai 2021
To get the scores for new data, you need to first get the outputs mu and coeff.
X = rand(100, 5);
XTrain = X(1:75, :)
XTest = X(76:100,:)
[coeff,scoreTrain,~,~,explained,mu] = pca(XTrain);
Now, to apply the same transformation, that is to get scores for new data, apply the following equation.
idx = 3; % Keep 3 principal components
scoreTest = (XTest-mu)*coeff(:,1:idx)
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