I'm not sure if I'm understanding correctly, but it reminds me of the kernel trick. The distances between the training samples and a target sample are computed

I'm not sure if I'm understanding correctly, but it reminds me of the kernel trick. The distances between the training samples and a target sample are computed, the distances are scaled through a kernel function, and the scaled distances are used as features. https://en.wikipedia.org/wiki/Kernel_method https://en.wikipedia.org/wiki/Kernel_method