Variable Kernel Density Estimation in High-Dimensional Feature Spaces

Christiaan Maarten van der Walt, Etienne Barnard

AAAI 2017 pp. 2674-2680

doi:10.1609/AAAI.V31I1.10885 /aaai/2017/vanderwalt2017aaai-variable/

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.

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Cite

Text

van der Walt and Barnard. "Variable Kernel Density Estimation in High-Dimensional Feature Spaces." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10885

Markdown

[van der Walt and Barnard. "Variable Kernel Density Estimation in High-Dimensional Feature Spaces." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/vanderwalt2017aaai-variable/) doi:10.1609/AAAI.V31I1.10885

BibTeX

@inproceedings{vanderwalt2017aaai-variable,
  title     = {{Variable Kernel Density Estimation in High-Dimensional Feature Spaces}},
  author    = {van der Walt, Christiaan Maarten and Barnard, Etienne},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {2674-2680},
  doi       = {10.1609/AAAI.V31I1.10885},
  url       = {https://mlanthology.org/aaai/2017/vanderwalt2017aaai-variable/}
}