Abstract
Originalsprache | englisch |
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Publikationsstatus | Veröffentlicht - 2019 |
Veranstaltung | Shape Modeling International (SMI) - Simon Fraser University, Vancouver, Kanada Dauer: 19 Jun 2019 → 21 Jun 2019 |
Konferenz
Konferenz | Shape Modeling International (SMI) |
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Land | Kanada |
Ort | Vancouver |
Zeitraum | 19/06/19 → 21/06/19 |
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A Kernel for Multi-Parameter Persistent Homology. / Corbet, René; Fugacci, Ulderico; Kerber, Michael; Landi, Claudia; Wang, Bei.
2019. Beitrag in Shape Modeling International (SMI), Vancouver, Kanada.Publikation: Konferenzbeitrag › Paper › Forschung › Begutachtung
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TY - CONF
T1 - A Kernel for Multi-Parameter Persistent Homology
AU - Corbet, René
AU - Fugacci, Ulderico
AU - Kerber, Michael
AU - Landi, Claudia
AU - Wang, Bei
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
M3 - Paper
ER -