pers.image: Persistence Image

In kernelTDA: Statistical Learning with Kernel for Persistence Diagrams

Description

Compute the Persistence Image for a given diagram, using piecewise linear weight functions and Gaussian baseline distribution.

Usage

pers.image(d1, nbins, dimension, h)

Arguments

d1	A persistence diagram, in the form of a matrix with 3 columns (first one is the dimension, second is the birth-time, last one is the death-time).
nbins	Number of bins for the discretization of the Persistence Surface into the Persistence Image.
dimension	Dimension of the topological features of interest (0 for connected components, 1 for cycles etc).
h	Standard deviation of the Gaussian baseline used to compute the Persistence Surface.

Value

a nbins x nbins matrix containing the Persistence Image.

Author(s)

Tullia Padellini

References

\insertRef

adams2017persistencekernelTDA

Examples

diag1 <- matrix(c(1,1,1,0,2,3,2,2.5,4), ncol = 3, byrow = FALSE)
colnames(diag1) <- c("dimension", "birth", "death")
pi1 <- pers.image(d1 = diag1, nbins = 20, dimension = 1, h = 1)
image(pi1) 

Example output

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