Memory-Effcient Orthogonal Least Squares Kernel Density Estimation Using Enhanced Empirical Cumulative Distribution Functions
Abstract
A novel training algorithm for sparse kernel density estimates by regression of the empirical cumulative density function (ECDF) is presented. It is shown how an overdetermined linear least-squares problem may be solved by a greedy forward selection procedure using updates of the orthogonal decomposition in an order-recursive manner. We also present a method for improving the accuracy of the estimated models which uses output-sensitive computation of the ECDF. Experiments show the superior performance of our proposed method compared to state-of-the-art density estimation methods such as Parzen windows, Gaussian Mixture Models, and ε-Support Vector Density models [1].
Cite
Text
Schaffoner et al. "Memory-Effcient Orthogonal Least Squares Kernel Density Estimation Using Enhanced Empirical Cumulative Distribution Functions." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Schaffoner et al. "Memory-Effcient Orthogonal Least Squares Kernel Density Estimation Using Enhanced Empirical Cumulative Distribution Functions." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/schaffoner2007aistats-memoryeffcient/)BibTeX
@inproceedings{schaffoner2007aistats-memoryeffcient,
title = {{Memory-Effcient Orthogonal Least Squares Kernel Density Estimation Using Enhanced Empirical Cumulative Distribution Functions}},
author = {Schaffoner, Martin and Andelic, Edin and Katz, Marcel and Krüger, Sven E. and Wendemuth, Andreas},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {428-435},
volume = {2},
url = {https://mlanthology.org/aistats/2007/schaffoner2007aistats-memoryeffcient/}
}