# Density-Based Clustering with Kernel Diffusion

arXiv: https://arxiv.org/abs/2110.05096 · HTML: https://mlsanity.com/paper/2110.05096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Linkage Based Face Clustering via Graph Convolution Network](https://mlsanity.com/paper/1903.11306/) (2019)
- [Learning to Cluster Faces on an Affinity Graph](https://mlsanity.com/paper/1904.02749/) (2019)
- [Learning to Cluster Faces via Confidence and Connectivity Estimation](https://mlsanity.com/paper/2004.00445/) (2020)
- [Clustering Millions of Faces by Identity](https://mlsanity.com/paper/1604.00989/) (2016)
- [Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition](https://mlsanity.com/paper/1809.01407/) (2018)

## Abstract (arXiv)

Finding a suitable density function is essential for density-based clustering algorithms such as DBSCAN and DPC. A naive density corresponding to the indicator function of a unit d-dimensional Euclidean ball is commonly used in these algorithms. Such density suffers from capturing local features in complex datasets. To tackle this issue, we propose a new kernel diffusion density function, which is adaptive to data of varying local distributional characteristics and smoothness. Furthermore, we develop a surrogate that can be efficiently computed in linear time and space and prove that it is asymptotically equivalent to the kernel diffusion density function. Extensive empirical experiments on benchmark and large-scale face image datasets show that the proposed approach not only achieves a significant improvement over classic density-based clustering algorithms but also outperforms the state-of-the-art face clustering methods by a large margin.