How to use scikit-learn PCA for features reduction and know which features are discarded

Question

I am trying to run a PCA on a matrix of dimensions m x n where m is the number of features and n the number of samples.

Suppose I want to preserve the nf fe

Answer 1

The projected features onto principal components will retain the important information (axes with maximum variances) and drop axes with small variances. This behavior is like to compression (Not discard).

And X_proj is the better name of X_new, because it is the projection of X onto principal components

You can reconstruct the X_rec as

X_rec = pca.inverse_transform(X_proj) # X_proj is originally X_new

Here, X_rec is close to X, but the less important information was dropped by PCA. So we can say X_rec is denoised.

In my opinion, I can say the noise is discard.