Computing the Approximate Convex Hull in High Dimensions
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Abstract (arXiv)
In this paper, an effective method with time complexity of O(K3/2N2log fracKε0) is introduced to find an approximation of the convex hull for N points in dimension n, where K is close to the number of vertices of the approximation. Since the time complexity is independent of dimension, this method is highly suitable for the data in high dimensions. Utilizing a greedy approach, the proposed method attempts to find the best approximate convex hull for a given number of vertices. The approximate convex hull can be a helpful substitute for the exact convex hull for on-line processes and applications that have a favorable trade off between accuracy and parsimony.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
3 papers.
- 2025Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network15 Sep 2025
- 2022Why Robust Natural Language Understanding is a Challenge21 Jun 2022
- 2021PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations5 Mar 2021