Multiclass Total Variation Clustering

TL;DR

This paper introduces a new framework for multiclass total variation clustering that outperforms previous methods and rivals state-of-the-art NMF approaches, avoiding recursive strategies.

Contribution

It proposes a non-recursive, general framework for multiclass total variation clustering, significantly improving performance over prior algorithms.

Findings

Outperforms previous total variation clustering methods

Comparable to state-of-the-art NMF algorithms

Effective for multiclass clustering without recursion

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.