ML Anthology

On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm

Ery Arias-Castro, David Mason, Bruno Pelletier
JMLR 2016 pp. 1-28
/jmlr/2016/ariascastro2016jmlr-estimation/

Abstract

We consider the problem of estimating the gradient lines of a density, which can be used to cluster points sampled from that density, for example via the mean-shift algorithm of Fukunaga and Hostetler (1975). We prove general convergence bounds that we then specialize to kernel density estimation.

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Cite

Text

Arias-Castro et al. "On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm." Journal of Machine Learning Research, 2016.

Markdown

[Arias-Castro et al. "On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/ariascastro2016jmlr-estimation/)

BibTeX

@article{ariascastro2016jmlr-estimation,
  title     = {{On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm}},
  author    = {Arias-Castro, Ery and Mason, David and Pelletier, Bruno},
  journal   = {Journal of Machine Learning Research},
  year      = {2016},
  pages     = {1-28},
  volume    = {17},
  url       = {https://mlanthology.org/jmlr/2016/ariascastro2016jmlr-estimation/}
}