kernelTDA: Statistical Learning with Kernel for Persistence Diagrams

Provides tools for exploiting topological information into standard statistical learning algorithms. To this aim, this package contains the most popular kernels defined on the space of persistence diagrams, and persistence images. Moreover, it provides a solver for kernel Support Vector Machines problems, whose kernels are not necessarily positive semidefinite, based on the C++ library 'LIBSVM' <>, and on its R implementation 'e1071'. Additionally, it allows to compute Wasserstein distance between persistence diagrams with an arbitrary ground metric, building an R interface for the C++ library 'HERA' <>.

Package details

AuthorTullia Padellini [aut, cre], Francesco Palini [aut], Pierpaolo Brutti [ctb], David Meyer [ctb, cph] (libsvm to R code (e1071 package)), Chih-Chung Chang [ctb, cph] (LIBSVM C++ code), Chih-Chen Lin [ctb, cph] (LIBSVM C++ code), Michael Kerber [ctb, cph] (HERA C++ code), Dmitriy Morozov [ctb, cph] (HERA C++ code), Arnur Nigmetov [ctb, cph] (HERA C++ code)
MaintainerTullia Padellini <[email protected]>
Package repositoryView on CRAN
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kernelTDA documentation built on July 3, 2019, 5:04 p.m.