Analysis and Application of the Generalized Mean-Shift Process

Yizong Cheng
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:102-111, 1995.

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 this Paper

BibTeX
@InProceedings{pmlr-vR0-cheng95a, 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}, pages = {102--111}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/cheng95a/cheng95a.pdf}, url = {https://proceedings.mlr.press/r0/cheng95a.html}, 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.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Analysis and Application of the Generalized Mean-Shift Process %A Yizong Cheng %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-cheng95a %I PMLR %P 102--111 %U https://proceedings.mlr.press/r0/cheng95a.html %V R0 %X 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. %Z Reissued by PMLR on 01 May 2022.
APA
Cheng, Y.. (1995). Analysis and Application of the Generalized Mean-Shift Process. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:102-111 Available from https://proceedings.mlr.press/r0/cheng95a.html. Reissued by PMLR on 01 May 2022.

Related Material