Summary of Convergence Analysis Of Mean Shift, by Ryoya Yamasaki et al.
Convergence Analysis of Mean Shift
by Ryoya Yamasaki, Toshiyuki Tanaka
First submitted to arxiv on: 15 May 2023
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The mean shift algorithm aims to find a mode of the kernel density estimate (KDE). This paper provides a convergence guarantee for the mode estimate sequence generated by the mean shift algorithm, along with an evaluation of its convergence rate. The findings extend existing results on analytic kernels and the Epanechnikov kernel, covering the biweight kernel which is optimal among non-negative kernels in terms of asymptotic statistical efficiency for KDE-based mode estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The mean shift algorithm tries to find a special point that makes a certain type of math problem more likely. This paper shows that this process always gets closer to the right answer and explains how fast it gets there. This is important because it helps us understand how to make predictions about where things will cluster together. |
