This R-package provides an implementation of the Geodesic Topological Kernels defined in Supervised Learning with indefinite topological Kernels, as well the most famous kernels
present in the framework of Topological Data Analysis (TDA), more
specifically:
- Persistence Scale Space Kernel
- Sliced Wasserstein Kernel
- Persistence Fisher Kernel
- Geodesic Wasserstein Kernel(s)
- Persistence Images
Here you can also find an R interface to the C++ library
HERA, which contains
an efficient implementation of the L_p q-Wasserstein distance
between persistence diagrams.
Finally, this package contains a solver for kernelized Support Vector Machine problems with indefinite kernels, based on the algorithm proposed by Loosli et al.. The implementation is largely based on the C++ library LIBSVM, and on its R interface in the package e1071.
This package is now on CRAN, you can install it with:
install.packages("kernelTDA")