Why are principal components in PCA (eigenvectors of the covariance matrix) mutually orthogonal?
9286
Questions on PCA: when are PCs independent? why is PCA sensitive to scaling? why are PCs constrained to be orthogonal?
101622
Choosing number of principal components to retain
114794
Is there any advantage of SVD over PCA?
145396
Symmetric Matrix , Eigenvectors are not orthogonal to the same eigenvalue.
21885
Why is the determinant useful when finding eigenvectors?
16641
Strange results of varimax rotation of principal component analysis in Stata: rotated components are all zeros and ones
111769
Why is inverse of orthogonal matrix is its transpose?
66995
Why is a projection matrix symmetric?
126886
Do the principal components change if we apply PCA more than once (recursively) on data?
97435
Reversing PCA back to the original variables
154261
PCA when the dimensionality is greater than the number of samples
81049
What norm of the reconstruction error is minimized by the low-rank approximation matrix obtained with PCA?
4244
How to compute PCA scores from eigendecomposition of the covariance matrix?
51682
Covariance matrix explanation
25410
What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)?
18012
Eigenvectors of real symmetric matrices are orthogonal
89237
PCA: Eigenvectors of opposite sign and not being able to compute eigenvectors with `solve` in R