6 citations · 24 across the 21 of their papers we have counts for
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math.AT2024★ 1 cited
Point-Level Topological Representation Learning on Point Clouds
Vincent P. Grande, Michael T. Schaub
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 Homol…
math.AT2023★ 5 cited
Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal
Vincent P. Grande, Michael T. Schaub
The rich spectral information of the graph Laplacian has been instrumental in graph theory, machine learning, and graph signal processing for applications such as graph classificat…
math.AT2023
Non-isotropic Persistent Homology: Leveraging the Metric Dependency of PH
Vincent P. Grande, Michael T. Schaub
Persistent Homology is a widely used topological data analysis tool that creates a concise description of the topological properties of a point cloud based on a specified filtratio…