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.
@article{arxiv.1306.1185,
title = {Multiclass Total Variation Clustering},
author = {Xavier Bresson and Thomas Laurent and David Uminsky and James H. von Brecht},
journal= {arXiv preprint arXiv:1306.1185},
year = {2013}
}