FastEx: Hash Clustering with Exponential Families
Abstract
Clustering is a key component in data analysis toolbox. Despite its importance, scalable algorithms often eschew rich statistical models in favor of simpler descriptions such as $k$-means clustering. In this paper we present a sampler, capable of estimating mixtures of exponential families. At its heart lies a novel proposal distribution using random projections to achieve high throughput in generating proposals, which is crucial for clustering models with large numbers of clusters.
Cite
Text
Ahmed et al. "FastEx: Hash Clustering with Exponential Families." Neural Information Processing Systems, 2012.Markdown
[Ahmed et al. "FastEx: Hash Clustering with Exponential Families." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/ahmed2012neurips-fastex/)BibTeX
@inproceedings{ahmed2012neurips-fastex,
title = {{FastEx: Hash Clustering with Exponential Families}},
author = {Ahmed, Amr and Ravi, Sujith and Smola, Alex J. and Narayanamurthy, Shravan M.},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {2798-2806},
url = {https://mlanthology.org/neurips/2012/ahmed2012neurips-fastex/}
}