Multiclass Total Variation Clustering
This addresses the limitation of recursive methods in multiclass clustering for data analysis, offering a more effective solution.
The paper tackled the problem of multiclass clustering using total variation, presenting a non-recursive framework that outperformed previous total variation algorithms and compared well with state-of-the-art NMF approaches.
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.