The EM Perspective of Directional Mean Shift Algorithm
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 44 references that are in the map. First posted on arXiv 25 Jan 2021.
Builds on
9 of 44 references.
References · 9
- 2014Statistical guarantees for the EM algorithm: From population to sample-based analysiscited by 133
- 2015A review of mean-shift algorithms for clusteringcited by 19
- 2018The modal age of Statisticscited by 9
- 2014On the Convergence of the Mean Shift Algorithm in the One-Dimensional Spacecited by 6
- 2013Nonparametric Inference For Density Modescited by 5
- 2020Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Datacited by 5
- 2013Exact risk improvement of bandwidth selectors for kernel density estimation with directional datacited by 4
- 2020Nonparametric estimation of directional highest density regionscited by 1
- 2020Nonparametric multimodal regression for circular datacited by 1
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Citing papers per year: 2021: 1.
1 paper.