Calculating principal component scores from principal component coefficients of the new data
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Amin Kassab-Bachi on 7 May 2021
I perfomed a PCA on dataset using the function
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
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.
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.
Aditya Patil on 12 May 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)
For more details, see the Apply PCA to New Data and Generate C/C++ Code documentation.