# Convergence and clustering analysis for Mean Shift with radially symmetric, positive definite kernels

arXiv: https://arxiv.org/abs/2506.19837 · HTML: https://mlsanity.com/paper/2506.19837/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [On the Convergence of the Mean Shift Algorithm in the One-Dimensional Space](https://mlsanity.com/paper/1407.2961/) (2014)
- [Convergence Analysis of Mean Shift](https://mlsanity.com/paper/2305.08463/) (2023)

## Abstract (arXiv)

The mean shift (MS) is a non-parametric, density-based, iterative algorithm with prominent usage in clustering and image segmentation. A rigorous proof for the convergence of its mode estimate sequence in full generality remains unknown. In this paper, we show that fortextit sufficiently large bandwidth convergence is guaranteed in any dimension with textitany radially symmetric and strictly positive definite kernels. Although the author acknowledges that our result is partially more restrictive than that of citeYT due to the lower limit of the bandwidth, our kernel class is not covered by the kernel class in citeYT, and the proof technique is different. Moreover, we show theoretically and experimentally that while for Gaussian kernel, accurate clustering at textitlarge bandwidths is generally impossible, it may still be possible for other radially symmetric, strictly positive definite kernels.