How does PCA represent all data with just a few principal components?
10466
Making sense of principal component analysis, eigenvectors & eigenvalues
139690
Which variables should I transform, center, and/or standardize in my data for Principal Component Analysis?
6590
PCA and proportion of variance explained
14465
"Normalizing" variables for SVD / PCA
87448
Why is a sample covariance matrix singular when sample size is less than number of variables?
126886
Do the principal components change if we apply PCA more than once (recursively) on data?
67557
Imputation of missing values for PCA
103877
The advantages and disadvantages of using Kaiser Rule to select the number of principal components
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?
49582
How to visualize what canonical correlation analysis does (in comparison to what principal component analysis does)?
158823
Principal component analysis "backwards": how much variance of the data is explained by a given linear combination of the variables?
116179
Is principal component regression (PCR) using principal component scores for regression?
135047
Why does PCA maximize total variance of the projection?
7632
Interpretation of biplots in principal components analysis
29381
How to perform dimensionality reduction with PCA in R
70917
What's the difference between principal component analysis and multidimensional scaling?