mlsanityData to 7 Oct 2026

Convergence Analysis of Mean Shift

arXiv 2305.08463 ↗First posted 15 May 2023stat.ML cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

The mean shift (MS) algorithm seeks a mode of the kernel density estimate (KDE). This study presents a convergence guarantee of the mode estimate sequence generated by the MS algorithm and an evaluation of the convergence rate, under fairly mild conditions, with the help of the argument concerning the Lojasiewicz inequality. Our findings extend existing ones covering analytic kernels and the Epanechnikov kernel. Those are significant in that they cover the biweight kernel, which is optimal among non-negative kernels in terms of the asymptotic statistical efficiency for the KDE-based mode estimation.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 68 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

10 of 68 references.

References · 10

  1. 2016Calculus of the exponent of Kurdyka-Lojasiewicz inequality and its applications to linear convergence of first-order methodscited by 35
  2. 2014Splitting methods with variable metric for KL functionscited by 26
  3. 2014Majorization-minimization procedures and convergence of SQP methods for semi-algebraic and tame programscited by 10
  4. 2014Nonparametric modal regressioncited by 8
  5. 2016Mixture model modal clusteringcited by 8
  6. 2017Maximum Number of Modes of Gaussian Mixturescited by 7
  7. 2014On the Convergence of the Mean Shift Algorithm in the One-Dimensional Spacecited by 6
  8. 2017On Convergence of Epanechnikov Mean Shiftcited by 6
  9. 2020Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithmcited by 3
  10. 2023Optimal Kernel for Kernel-Based Modal Statistical Methodscited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 4.

6 papers.

  1. 2024Contrastive Mean-Shift Learning for Generalized Category Discoverycited by 39
  2. 2025Weighted quantization using MMD: From mean field to mean shift via gradient flowscited by 10
  3. 2023Sketch and shift: a robust decoder for compressive clusteringcited by 1
  4. 2025Unleashing the Potential of All Test Samples: Mean-Shift Guided Test-Time Adaptationcited by 1
  5. 2025Local Averaging Accurately Distills Manifold Structure From Noisy Datacited by 0
  6. 2025Convergence and clustering analysis for Mean Shift with radially symmetric, positive definite kernelscited by 0