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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2014
- Accession Number
- ADA612811
Entities
People
- David Uminsky
- James H. Von Brecht
- Laurent Thomas
- Xavier Bresson
Organizations
- University of California, Los Angeles