Gromov‐Hausdorff Stable Signatures for Shapes using Persistence 论文
2009Computer Graphics Forum引用 226
Topological and Geometric Data AnalysisAdvanced Neuroimaging Techniques and ApplicationsCell Image Analysis Techniques
摘要
Abstract We introduce a family of signatures for finite metric spaces, possibly endowed with real valued functions, based on the persistence diagrams of suitable filtrations built on top of these spaces. We prove the stability of our signatures under Gromov‐Hausdorff perturbations of the spaces. We also extend these results to metric spaces equipped with measures. Our signatures are well‐suited for the study of unstructured point cloud data, which we illustrate through an application in shape classification.