How can I get principal components by hand from covariance matrix?
4244
How to compute PCA scores from eigendecomposition of the covariance matrix?
27102
Combining two covariance matrices
70245
Why is the eigenvector in PCA taken to be unit norm?
117702
How to simulate random correlation matrix containing off diagonal structures
18487
Why are there only $n-1$ principal components for $n$ data if the number of dimensions is $\ge n$?
156166
PCA of non-Gaussian data
135439
Covariance matrix proposal distribution
38169
Bounds on off-diagonal entries of a correlation matrix
14465
"Normalizing" variables for SVD / PCA
123925
Grabbing the independent columns of a matrix A
24814
Is PCA still done via the eigendecomposition of the covariance matrix when dimensionality is larger than the number of observations?
113346
What is the proper association measure of a variable with a PCA component (on a biplot / loading plot)?
67557
Imputation of missing values for PCA
7779
How does centering the data get rid of the intercept in regression and PCA?
70640
Recovering eigenvectors from SVD
17765
How to use SVD for dimensionality reduction to reduce the number of columns (features) of the data matrix?
143908
What are "rotated" and "unrotated" principal components, given that PCA always rotates the coordinates axes?