# Multiclass Total Variation Clustering

arXiv: https://arxiv.org/abs/1306.1185 · HTML: https://mlsanity.com/paper/1306.1185/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2013.

## Built on by (most cited first, 7 of 7)

- [Elastic Graph Neural Networks](https://mlsanity.com/paper/2107.06996/) (2021)
- [Large sample spectral analysis of graph-based multi-manifold clustering](https://mlsanity.com/paper/2107.13610/) (2021)
- [Graph-based Active Learning for Semi-supervised Classification of SAR Data](https://mlsanity.com/paper/2204.00005/) (2022)
- [Total Variation Graph Neural Networks](https://mlsanity.com/paper/2211.06218/) (2022)
- [Graph Laplacian for Semi-Supervised Learning](https://mlsanity.com/paper/2301.04956/) (2023)
- [BASiS: Batch Aligned Spectral Embedding Space](https://mlsanity.com/paper/2211.16960/) (2022)
- [A Goemans-Williamson type algorithm for identifying subcohorts in clinical trials](https://mlsanity.com/paper/2506.10879/) (2025)

## Abstract (arXiv)

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.