June 5, 2024, 4:44 a.m. | Vincent P. Grande, Michael T. Schaub

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02300v1 Announce Type: cross
Abstract: Topological Data Analysis (TDA) allows us to extract powerful topological and higher-order information on the global shape of a data set or point cloud. Tools like Persistent Homology or the Euler Transform give a single complex description of the global structure of the point cloud. However, common machine learning applications like classification require point-level information and features to be available. In this paper, we bridge this gap and propose a novel method to extract node-level …

abstract analysis arxiv cloud cs.cg cs.lg data data analysis data set extract global however information machine math.at node representation representation learning set shape tools type

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