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 | Unveröffentlicht - 2018 |
Veranstaltung | Algebraic Topology: Methods, Computation and Science - IST Austria, Klosterneuburg, Österreich Dauer: 25 Juni 2018 → 29 Juni 2018 Konferenznummer: 8 https://ist.ac.at/atmcs8/welcome/ |
Konferenz
Konferenz | Algebraic Topology: Methods, Computation and Science |
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Kurztitel | ATMCS |
Land/Gebiet | Österreich |
Ort | Klosterneuburg |
Zeitraum | 25/06/18 → 29/06/18 |
Internetadresse |