Finding original features' effect to the principal components used as inputs in Kernel PCA
Since you are applying PCA in the kernel space, there is a strictly nonlinear relation with your original features and the features of the reduced data; the eigenvectors you calculate are in the kernel space to begin with. This obstructs a straightforward approach, but maybe you can do some kind of sensitivity analysis. Apply small perturbations to the original features and measure how the final, reduced features react to them. The Jacobian of the final features with respect to the original features can be also a good place to start.
I am trying to implement Kernel PCA to my dataset which has both categorical (encoded with one hot encoder) and numeric features and decreases the number of dimensions from 22 to 3 dimensions in total. After that, I will continue with clustering implementation. I use Spyder as IDE.
In order to understand the structure of my yielded clusters from the algorithm, I want to interpret which features affect the derived principal components and how they affect them.
Is it possible? If so, how can I interpret this, is there any method?