Agglomerative Multivariate Information Bottleneck
Abstract
The information bottleneck method is an unsupervised model independent data organization technique. Given a joint distribution peA, B), this method con(cid:173) structs a new variable T that extracts partitions, or clusters, over the values of A that are informative about B. In a recent paper, we introduced a general princi(cid:173) pled framework for multivariate extensions of the information bottleneck method that allows us to consider multiple systems of data partitions that are inter-related. In this paper, we present a new family of simple agglomerative algorithms to construct such systems of inter-related clusters. We analyze the behavior of these algorithms and apply them to several real-life datasets.
Cite
Text
Slonim et al. "Agglomerative Multivariate Information Bottleneck." Neural Information Processing Systems, 2001.Markdown
[Slonim et al. "Agglomerative Multivariate Information Bottleneck." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/slonim2001neurips-agglomerative/)BibTeX
@inproceedings{slonim2001neurips-agglomerative,
title = {{Agglomerative Multivariate Information Bottleneck}},
author = {Slonim, Noam and Friedman, Nir and Tishby, Naftali},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {929-936},
url = {https://mlanthology.org/neurips/2001/slonim2001neurips-agglomerative/}
}