Abstract
Topological data analysis and its main method, persistent homology, provide a toolkit for computing topological information of high-dimensional and noisy data sets. Kernels for one-parameter persistent homology have been established to connect persistent homology with machine learning techniques. We contribute a kernel construction for multi-parameter persistence by integrating a one-parameter kernel weighted along straight lines. We prove that our kernel is stable and efficiently computable, which establishes a theoretical connection between topological data analysis and machine learning for multivariate data analysis.
Originalsprache | englisch |
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Publikationsstatus | Veröffentlicht - 2019 |
Veranstaltung | Shape Modeling International (SMI) - Simon Fraser University, Vancouver, Kanada Dauer: 19 Juni 2019 → 21 Juni 2019 |
Konferenz
Konferenz | Shape Modeling International (SMI) |
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Land/Gebiet | Kanada |
Ort | Vancouver |
Zeitraum | 19/06/19 → 21/06/19 |