Mean field models for large data-clustering problems
Numerical Analysis
2020-03-16 v2 Numerical Analysis
Data Analysis, Statistics and Probability
Abstract
We consider mean-field models for data--clustering problems starting from a generalization of the bounded confidence model for opinion dynamics. The microscopic model includes information on the position as well as on additional features of the particles in order to develop specific clustering effects. The corresponding mean--field limit is derived and properties of the model are investigated analytically. In particular, the mean--field formulation allows the use of a random subsets algorithm for efficient computations of the clusters. Applications to shape detection and image segmentation on standard test images are presented and discussed.
Cite
@article{arxiv.1907.03585,
title = {Mean field models for large data-clustering problems},
author = {Michael Herty and Lorenzo Pareschi and Giuseppe Visconti},
journal= {arXiv preprint arXiv:1907.03585},
year = {2020}
}