Clustering by Hill-Climbing: Consistency Results
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Abstract (arXiv)
We consider several hill-climbing approaches to clustering as formulated by Fukunaga and Hostetler in the 1970's. We study both continuous-space and discrete-space (i.e., medoid) variants and establish their consistency.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 47 references that are in the map. First posted on arXiv 18 Feb 2022.
Builds on
10 of 47 references.
References · 10
- 2017Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolutioncited by 63
- 2014A Population Background for Nonparametric Density-Based Clusteringcited by 14
- 2018Quickshift++: Provably Good Initializations for Sample-Based Mean Shiftcited by 14
- 2015Density Level Sets: Asymptotics, Inference, and Visualizationcited by 11
- 2021MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Trackingcited by 9
- 2017On the Consistency of Quick Shiftcited by 7
- 2013Nonparametric Inference For Density Modescited by 5
- 2015Statistical Inference using the Morse-Smale Complexcited by 4
- 2021Space Partitioning and Regression Mode Seeking via a Mean-Shift-Inspired Algorithmcited by 2
- 2021An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Treecited by 1
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.