Why and when would one begin with PCA on $X$ when predicting $y$?
52047
How can top principal components retain the predictive power on a dependent variable (or even lead to better predictions)?
43501
Interpretation of multivariate conditional gaussian function form?
85010
What're the differences between PCA and autoencoder?
84446
How to project a new vector onto PCA space?
135366
Why does the linear regression of "x on y" intersect with the linear regression of "y on x" at (x̄, ȳ)?
85031
How to interpret this PCA biplot to determine which attributes to pick?
154899
Is it valid to perform PCA if Kaiser-Meyer-Olkin (KMO) index is very low?
11188
Can principal component analysis be applied to datasets containing a mix of continuous and categorical variables?
143679
Is the sign of principal components meaningless?
154757
One component in PCA is always the mean vector in two-dimensions but not three
155433
Examples of PCA where PCs with low variance are "useful"
22551
What can cause PCA to worsen results of a classifier?
6206
How does centering make a difference in PCA (for SVD and eigen decomposition)?
70245
Why is the eigenvector in PCA taken to be unit norm?
126136
Fundamental question about dimensional analysis
41489
Why $p(y_n|y_{0:n-1})$ is a constant in Kalman filter derivation?
126886
Do the principal components change if we apply PCA more than once (recursively) on data?