# Deep Divergence-Based Approach to Clustering

arXiv: https://arxiv.org/abs/1902.04981 · HTML: https://mlsanity.com/paper/1902.04981/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 13 of 13)

- [Reconsidering Representation Alignment for Multi-view Clustering](https://mlsanity.com/paper/2103.07738/) (2021)
- [This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation](https://mlsanity.com/paper/2108.12204/) (2021)
- [Deep Bregman Divergence for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2109.07455/) (2021)
- [On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering](https://mlsanity.com/paper/2303.09877/) (2023)
- [Simplifying Clustering with Graph Neural Networks](https://mlsanity.com/paper/2207.08779/) (2022)
- [Total Variation Graph Neural Networks](https://mlsanity.com/paper/2211.06218/) (2022)
- [A Clustering-guided Contrastive Fusion for Multi-view Representation Learning](https://mlsanity.com/paper/2212.13726/) (2022)
- [The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making](https://mlsanity.com/paper/2301.08970/) (2023)
- [Uniform Consistency in Nonparametric Mixture Models](https://mlsanity.com/paper/2108.14003/) (2021)
- [MODIS: Multi-Omics Data Integration for Small and unpaired datasets](https://mlsanity.com/paper/2503.18856/) (2025)
- [Deep Metric Tensor Regularized Policy Gradient](https://mlsanity.com/paper/2305.11017/) (2023)
- [Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications](https://mlsanity.com/paper/2405.04061/) (2024)
- [Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise](https://mlsanity.com/paper/2602.22568/) (2026)

## Abstract (arXiv)

A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of research is in its infancy, and how to design and optimize suitable loss functions to train deep neural networks for clustering is still an open question. Our contribution to this emerging field is a new deep clustering network that leverages the discriminative power of information-theoretic divergence measures, which have been shown to be effective in traditional clustering. We propose a novel loss function that incorporates geometric regularization constraints, thus avoiding degenerate structures of the resulting clustering partition. Experiments on synthetic benchmarks and real datasets show that the proposed network achieves competitive performance with respect to other state-of-the-art methods, scales well to large datasets, and does not require pre-training steps.