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.
Original language | English |
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Publication status | Unpublished - 2018 |
Event | 34th International Symposium on Computational Geometry: SoCG 2018 - Budapest, Hungary Duration: 11 Jun 2018 → 14 Jun 2018 |
Conference
Conference | 34th International Symposium on Computational Geometry |
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Country/Territory | Hungary |
City | Budapest |
Period | 11/06/18 → 14/06/18 |
Keywords
- cs.LG
- cs.CG
- math.AT
- stat.ML