I have a question about the following line of KernelPCA. This line appears in the reprojection of the sample points in KernelPCA but for me, this line seems unnecessary. Here, kernel ridge regression is used to (approximately) reproject the "transformed samples"(, which are coefficients of principal components), using precomputed dual coefficients of the kernel ridge regression. Though we need to add alpha to the diagonal elements of the kernel matrix when computing the dual coefficients (as in _fit_inverse_transform), we do not need to add alpha to the kernel in the prediction stage, as far as I understand.

Is this line really necessary? If so, I would appreciate it if someone could explain why this line is needed.