Hyperkernel Based Density Estimation

We focus on solving the problem of learning an optimal smoothing kernel for the unsupervised learning problem of kernel density estimation(KDE) by using hyper-kernels. The optimal kernel is the one which minimizes the regularized negative leave-one-out-log likelihood score of the train set. We demonstrate that ”fixed bandwidth ” and ”variable bandwidth ” KDE are special cases of our algorithm.