@conference{275, keywords = {Kernel bandwidth, Kernel density, High-dimensional Feature Spaces}, author = {Christiaan Van der Walt and Etienne Barnard}, title = {Variable Kernel Density Estimation in High-dimensional Feature Spaces}, abstract = {Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dimensional feature spaces. We derive a variable kernel bandwidth estimator by minimizing the leave-one-out entropy objective function and show that this estimator is capable of performing estimation in high-dimensional feature spaces with great success. We compare the performance of this estimator to state-of-the art maximum likelihood estimators on a number of representative high-dimensional machine learning tasks and show that the newly introduced minimum leave-one-out entropy estimator performs optimally on a number of high-dimensional datasets considered.}, year = {2017}, journal = {AAAI Conf. on Artificial Intelligence (AAAI-17)}, chapter = {2674-2680}, month = {04/02-09/04}, }