The EM Perspective of Directional Mean Shift Algorithm
Statistics Theory
2021-01-26 v1 Methodology
Machine Learning
Statistics Theory
Abstract
The directional mean shift (DMS) algorithm is a nonparametric method for pursuing local modes of densities defined by kernel density estimators on the unit hypersphere. In this paper, we show that any DMS iteration can be viewed as a generalized Expectation-Maximization (EM) algorithm; in particular, when the von Mises kernel is applied, it becomes an exact EM algorithm. Under the (generalized) EM framework, we provide a new proof for the ascending property of density estimates and demonstrate the global convergence of directional mean shift sequences. Finally, we give a new insight into the linear convergence of the DMS algorithm.
Cite
@article{arxiv.2101.10058,
title = {The EM Perspective of Directional Mean Shift Algorithm},
author = {Yikun Zhang and Yen-Chi Chen},
journal= {arXiv preprint arXiv:2101.10058},
year = {2021}
}