If number of samples is smaller than number of features, how can all the variance in PCA be explained by less than $n$ components?
18487
Why are there only $n-1$ principal components for $n$ data if the number of dimensions is $\ge n$?
143724
Is it possible to use kernel PCA for feature selection?
4763
When generating samples using variational autoencoder, we decode samples from $N(0,1)$ instead of $\mu + \sigma N(0,1)$
48567
Skewed variables in PCA or factor analysis
55847
PCA demeaning the data
87448
Why is a sample covariance matrix singular when sample size is less than number of variables?
82345
Distribution of the magnitude of samples from the multivariate gaussian distribution
36609
Creating a single index from several principal components or factors retained from PCA/FA
113346
What is the proper association measure of a variable with a PCA component (on a biplot / loading plot)?
2632
PCA and the train/test split
143081
How to choose the right number of CV folds for the nested cross validation for a small sample?
118103
Does PCA's reconstruction error get reduced with more PCs being used?
25410
What is an intuitive explanation for how PCA turns from a geometric problem (with distances) to a linear algebra problem (with eigenvectors)?
101622
Choosing number of principal components to retain
79763
What do empty (missing) values in the output of PCA or factor analysis mean in R?
18671
Subsample of a random sample: random sample?
36477
Is there any required amount of variance captured by PCA in order to do later analyses?