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 with applicability on shape analysis, recognition and classification. 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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Seiten (von - bis) | 100005-100016 |
Seitenumfang | 11 |
Fachzeitschrift | Computers & Graphics: X |
Jahrgang | 2019 |
Ausgabenummer | 2 |
DOIs | |
Publikationsstatus | Veröffentlicht - 1 Dez. 2019 |