Locally Adaptive Kernel Estimation Using Sparse Functional Programming

Reproducing Kernel Hilbert Space (RKHS)-based methods are widely used in signal processing and machine learning applications. Yet, they suffer from a parameter selection issue: selecting the RKHS in which to operate (or even the kernel parameter) is often a significant challenge. Moreover, since the RKHS determines properties such as shape and smoothness of the learned function, its choice affects the effectiveness of these techniques. Likewise,...