Geometric Clustering Using the Information Bottleneck Method
Abstract
We argue that K–means and deterministic annealing algorithms for geo- metric clustering can be derived from the more general Information Bot- tleneck approach. If we cluster the identities of data points to preserve information about their location, the set of optimal solutions is massively degenerate. But if we treat the equations that define the optimal solution as an iterative algorithm, then a set of “smooth” initial conditions selects solutions with the desired geometrical properties. In addition to concep- tual unification, we argue that this approach can be more efficient and robust than classic algorithms.
Cite
Text
Still et al. "Geometric Clustering Using the Information Bottleneck Method." Neural Information Processing Systems, 2003.Markdown
[Still et al. "Geometric Clustering Using the Information Bottleneck Method." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/still2003neurips-geometric/)BibTeX
@inproceedings{still2003neurips-geometric,
title = {{Geometric Clustering Using the Information Bottleneck Method}},
author = {Still, Susanne and Bialek, William and Bottou, Léon},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {1165-1172},
url = {https://mlanthology.org/neurips/2003/still2003neurips-geometric/}
}