English

Preprocessing power weighted shortest path data using a s-Well Separated Pair Decomposition

Computer Vision and Pattern Recognition 2021-05-18 v2 Computational Geometry Data Structures and Algorithms Machine Learning

Abstract

For ss >> 0, we consider an algorithm that computes all ss-well separated pairs in certain point sets in Rn\mathbb{R}^{n}, nn >1>1. For an integer KK >1>1, we also consider an algorithm that is a permutation of Dijkstra's algorithm, that computes KK-nearest neighbors using a certain power weighted shortest path metric in Rn\mathbb{R}^{n}, nn >> 11. We describe each algorithm and their respective dependencies on the input data. We introduce a way to combine both algorithms into a fused algorithm. Several open problems are given for future research.

Keywords

Cite

@article{arxiv.2103.11216,
  title  = {Preprocessing power weighted shortest path data using a s-Well Separated Pair Decomposition},
  author = {Gurpreet S. Kalsi and Steven B. Damelin},
  journal= {arXiv preprint arXiv:2103.11216},
  year   = {2021}
}
R2 v1 2026-06-24T00:23:00.902Z