Title

Multiclass Total Variation Clustering

Authors

X Bresson
T Laurent
David Uminsky, University of San FranciscoFollow
J von Brecht

Document Type

Conference Proceeding

Publication Date

2013

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.

Comments

Presented at: 27th Annual Conference on Neural Information Processing Systems, NIPS 2013; Lake Tahoe, NV; United States; 5 December 2013 through 10 December 2013

Published by MIT Press

Recommended Citation

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.