Kernel PCA
Published on: January 2, 2022
Kernel PCA is an extension of PCA that allows for the separability of nonlinear data by making use of kernels. The basic idea behind it is to project the linearly inseparable data onto a higher dimensional space where it becomes linearly separable.
Kernel PCA can be summarized as a 4 step process [1]:
 Construct the kernel matrix
from the training dataset
 If the projected dataset
doesn’t have zero mean use the Gram matrix to substitute the kernel matrix .

Use
to solve for the vector . 
Compute the kernel principal components
[1] Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models