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.
Bresson, X., Laurent, T., Uminsky, D., Von Brecht, J.H.(2013). Multiclass total variation clustering. Advances in Neural Information Processing Systems 26 (NIPS 2013), 1421-1429.