Multiclass Total Variation Clustering
Abstract
Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation. While these algorithms perform well for bi-partitioning tasks, their recursive extensions yield unimpressive results for multiclass clustering tasks. This paper presents a general framework for multiclass total variation clustering that does not rely on recursion. The results greatly outperform previous total variation algorithms and compare well with state-of-the-art NMF approaches.
Cite
Text
Bresson et al. "Multiclass Total Variation Clustering." Neural Information Processing Systems, 2013.Markdown
[Bresson et al. "Multiclass Total Variation Clustering." Neural Information Processing Systems, 2013.](https://mlanthology.org/neurips/2013/bresson2013neurips-multiclass/)BibTeX
@inproceedings{bresson2013neurips-multiclass,
title = {{Multiclass Total Variation Clustering}},
author = {Bresson, Xavier and Laurent, Thomas and Uminsky, David and von Brecht, James},
booktitle = {Neural Information Processing Systems},
year = {2013},
pages = {1421-1429},
url = {https://mlanthology.org/neurips/2013/bresson2013neurips-multiclass/}
}