Matti Karppa

DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search

Kernel Density Estimation (KDE) is a nonparametric method for estimatig the shape of a density function, given a set of samples from the distribution. Recently, locality-sensitive hashing, originally proposed as a tool for nearest neighbor search, has been shown to enable fast KDE data structures. H…