Statistical Inference using the Morse-Smale Complex
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Abstract (arXiv)
The Morse-Smale complex of a function f decomposes the sample space into cells where f is increasing or decreasing. When applied to nonparametric density estimation and regression, it provides a way to represent, visualize, and compare multivariate functions. In this paper, we present some statistical results on estimating Morse-Smale complexes. This allows us to derive new results for two existing methods: mode clustering and Morse-Smale regression. We also develop two new methods based on the Morse-Smale complex: a visualization technique for multivariate functions and a two-sample, multivariate hypothesis test.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Jun 2015.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2021Moving Up the Cluster Tree with the Gradient Flowcited by 3
- 2022Clustering by Hill-Climbing: Consistency Resultscited by 2
- 2021An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Treecited by 1
- 2026Stable Density Ridges: Consistency and Convergence of Subspace Constrained Mean Shiftcited by 0
- 2026Stable Density Ridges: Consistency and Convergence of Subspace Constrained Mean Shift5 Aug 2026
- 2022Clustering by Hill-Climbing: Consistency Results18 Feb 2022
- 2021An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Tree19 Nov 2021
- 2021Moving Up the Cluster Tree with the Gradient Flow17 Sep 2021