# Graph skeletonization of high-dimensional point cloud data via topological method

@article{Magee2021GraphSO, title={Graph skeletonization of high-dimensional point cloud data via topological method}, author={Lucas Magee and Yusu Wang}, journal={ArXiv}, year={2021}, volume={abs/2109.07606} }

Geometric graphs form an important family of hidden structures behind data. In this paper, we develop an efficient and robust algorithm to infer a graph skeleton behind a point cloud dataset (PCD) embedded in high-dimensional space. Previously, there has been much work to recover a hidden graph from a low-dimensional density field, or from a relatively clean high-dimensional PCD (in the sense that the input points are within a small bounded distance to a true hidden graph). Our proposed… Expand

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