sw.kernel: Persistence Sliced Wasserstein Kernel (SWK)

In kernelTDA: Statistical Learning with Kernel for Persistence Diagrams

Description

Computes the Persistence Sliced Wasserstein Kernel (SWK) between persistence diagrams.

Usage

sw.kernel(d1, d2 = NULL, h, dimension, M = 10)

Arguments

d1	A persistence diagram (matrix with 3 col where the first one is the dimension, the second is the birth-time and the third is the death-time) or a list of diagrams
d2	A persistence diagram (matrix with 3 col where the first one is the dimension, the second is the birth-time and the third is the death-time).
h	bandwidth of the kernel
dimension	The dimension of the topological feature (0 for connected components, 1 for cycles etc)
M	number of directions on which to approximate the Sliced Wasserstein Distance

Value

If d1 is a list of Persistence Diagrams, this function returns a matrix whose (i,j) entry is the SWK computed in (d1[[i]], d2[[j]]), otherwise it returns the value for the SWK computed in (d1, d2)

Author(s)

Tullia Padellini

References

\insertRef

carriere2017slicedkernelTDA

Examples

diag1 <- matrix(c(1,1,1,0,2,3,2,2.5,4), ncol = 3, byrow = FALSE)
diag2 <- matrix(c(1,1,0,1,1,2), ncol = 3, byrow = FALSE)
sw.kernel(d1 = diag1, d2 = diag2, h = 1, dimension = 1)

kernelTDA documentation built on April 19, 2020, 3:56 p.m.