How does PCA behave when number of features is greater than number of samples?

PCA still works but maximum number of meaningful components is limited to number of samples minus one.

When features (p) exceed samples (n), covariance matrix becomes rank-deficient. PCA leverages SVD, where only min(n-1, p) components carry non-zero variance. This makes dimensionality reduction especially useful in such cases.