so im using pca to reduce the number of joints of a 3d skeletal animation; the algorithm works fine on the input I give it (as in, i get sets of eigen values, vectors and can transform my data or whatever). the problem is, im not quite sure how to use PCA to get back my 'transformed' original data less the components i drop out. this is basically whats going on: i can represent my 3d animation data in either of these two formats: A) a single angel (float) describing how far away the joint/controller is from its natural position B) euler x,y,z angels describing the rotations needed to form the joint/controller i take either A or B and throw it into a matrix. in the case of A the matrix has X1,Y1,Z1,X2,Y2,Z2,X3,Y3,Z3...... columns for all joints, and the rows correspond to my animation frames; if I use B), then its just A1, A2, A3, A4.... columns for all joints, and again the rows represent the frames. i calculate the symmetric covariance matrix for this data after subtracting the mean (per columns) from each data member. householder reduction then tridiagonal ql is used. so now, i have a bunch of eigen values and vectors, ordered from smallest to greatest: suppose i decide to not include the smallest 3 eigenvectors in my feature set. the question now is, given what I have, how do I figure out which columns of my *original data* to discard? the eigenvalues and vectors were calculated for the covariance matrix, and even if I zero out some of the eigenvectors and multiply the corresponding feature matrix by my original data.. im not left with any indication to which column of my original feature set is reasonably safe to discard. Any assistance will be greatly appreciated!

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