Density Estimation Using Mixtures of Mixtures of Gaussians
Abstract
In this paper we present a new density estimation algorithm using mixtures of mixtures of Gaussians. The new algorithm overcomes the limitations of the popular Expectation Maximization algorithm. The paper first introduces a new model selection criterion called the Penalty-less Information Criterion, which is based on the Jensen-Shannon divergence. Mean-shift is used to automatically initialize the means and covariances of the Expectation Maximization in order to obtain better structure inference. Finally, a locally linear search is performed using the Penalty-less Information Criterion in order to infer the underlying density of the data. The validity of the algorithm is verified using real color images.
Cite
Text
Abd-Almageed and Davis. "Density Estimation Using Mixtures of Mixtures of Gaussians." European Conference on Computer Vision, 2006. doi:10.1007/11744085_32Markdown
[Abd-Almageed and Davis. "Density Estimation Using Mixtures of Mixtures of Gaussians." European Conference on Computer Vision, 2006.](https://mlanthology.org/eccv/2006/abdalmageed2006eccv-density/) doi:10.1007/11744085_32BibTeX
@inproceedings{abdalmageed2006eccv-density,
title = {{Density Estimation Using Mixtures of Mixtures of Gaussians}},
author = {Abd-Almageed, Wael and Davis, Larry S.},
booktitle = {European Conference on Computer Vision},
year = {2006},
pages = {410-422},
doi = {10.1007/11744085_32},
url = {https://mlanthology.org/eccv/2006/abdalmageed2006eccv-density/}
}