Class
Article
College
College of Science
Department
English Department
Faculty Mentor
Kevin Moon
Presentation Type
Poster Presentation
Abstract
The problem of estimating a probability density function from data has many applications in machine learning and data science. Nonparametric estimators are useful in this context as they require relatively few assumptions on the densities. Unfortunately, standard nonparametric methods such as kernel density estimationtend to converge slowly to the true value in high dimensions as a function of the data sample size. Recent work has shown that optimally weighted ensembles of nonparametric estimators can be used to achieve a fast convergence rate when estimating information theoretic functionals such as information divergence. We explore the extension of this theory to density estimation to derive a nonparametric kernel density estimator that converges to the true density function quickly.
Location
Logan, UT
Start Date
4-6-2022 12:00 AM
Included in
Ensemble Kernel Density Estimation
Logan, UT
The problem of estimating a probability density function from data has many applications in machine learning and data science. Nonparametric estimators are useful in this context as they require relatively few assumptions on the densities. Unfortunately, standard nonparametric methods such as kernel density estimationtend to converge slowly to the true value in high dimensions as a function of the data sample size. Recent work has shown that optimally weighted ensembles of nonparametric estimators can be used to achieve a fast convergence rate when estimating information theoretic functionals such as information divergence. We explore the extension of this theory to density estimation to derive a nonparametric kernel density estimator that converges to the true density function quickly.