Analysis and Application of the Generalized Mean-Shift Process
Abstract
The mean shift process repeatedly moves each data point to the mean of data points in its neighborhood. This process is generalized and analyzed. Its relation with maximum-entropy and $\mathrm{K}$-means clustering methods is studied. Its nature of gradient mapping is revealed. Its applications in clustering, Hough transform, and overfitting relaxation are examined.
Cite
Text
Cheng. "Analysis and Application of the Generalized Mean-Shift Process." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[Cheng. "Analysis and Application of the Generalized Mean-Shift Process." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/cheng1995aistats-analysis/)BibTeX
@inproceedings{cheng1995aistats-analysis,
title = {{Analysis and Application of the Generalized Mean-Shift Process}},
author = {Cheng, Yizong},
booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
year = {1995},
pages = {102-111},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/cheng1995aistats-analysis/}
}