About the PCA in section 3.1 of the paper.

[Bvvq]

Hi, thank you for releasing the code. I have a question that looking forward to your answers：
This PCA code, in my opinion, reduces the dimensionality of the features (K*K) and proves the redundancy of the features within each kernel, how is the intra-kernel correlations derived from this?

step 1: split 3D kernel F into 2D kernels (assuming F is of size CxHxW)

xs = [F[nChannel, :, :].flatten() for nChannel in range(F.shape[0])]
X = np.array(xs)

step 2: perform PCA

import sklearn.decomposition
pca = sklearn.decomposition.PCA(n_components=None)
pca.fit(X)

step3: this is the variance of F which is explained by the first principal component (PC1)

v = pca.explained_variance_ratio_[0]