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- W2893726797 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." @default.
- W2893726797 created "2018-10-05" @default.
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- W2893726797 date "2018-09-26" @default.
- W2893726797 modified "2023-09-27" @default.
- W2893726797 title "A Kernel for Multi-Parameter Persistent Homology" @default.
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